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text opment of video information and wide usage of video analysis in all directions.\n
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text With the capacity of depicting video at the semantic level, video annotation has\n
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text numerous applications in video analysis. Due to the shortcomings present in\n
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text manual video annotation, Automatic Video Annotation was introduced. In this\n
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text paper, distinctive methodologies of automatic video annotation are discussed.\n
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text These models are classified into five classes namely, (1) Generative models,\n
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text cussed. Additionally, the future directions concerning the research aspect of\n
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text Keywords: Automatic Video Annotation (AVA) \u0001 Deep learning \u0001 Ontology \u0001\n
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text Feature extraction \u0001 Convolutional Neural Network (CNN)\n
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text 1 Introduction\n
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text With the recent development of online video data sharing platforms, for example,\n
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text YouTube, the rate of growth and collection of video data have developed quickly and\n
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text possess a major impact in day-to-day life. By the conventional video annotation sys-\n
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text tems marking video captions or comments at the semantic level, are not feasible with\n
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text enormous data available. The primary disadvantage of manual video annotations is\n
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text © Springer Nature Switzerland AG 2020\n
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text A. Abraham et al. (Eds.): ISDA 2018, AISC 940, pp. 1060–1069, 2020.\n
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text https://doi.org/10.1007/978-3-030-16657-1_99\n
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text In recent days, AVA techniques have gained attention into a new dimension\n
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text because of the shortcoming existing in the traditional annotation model. In [1], method\n
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text driven by the multiple Bernoulli relevance models is proposed to carry on the anno-\n
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text tating video by unsupervised model. These accomplishments have supported the\n
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text advancement of video annotation techniques. Similarly, the annotation for the input\n
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text video can be predicted from the annotated dataset through the unsupervised mode or\n
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text between low-level features in the video and high-level semantic labels. Annotation can\n
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text encourage video search using the content. Content-based video retrieval (CBVR) can\n
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text be considered semantically significant than to perform without any query because the\n
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text automated mapping between labels and video frames can be trusted [1–3]. The process\n
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text of Automatic video annotation is presented in Fig. 1.\n
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text In this paper, five AVA strategies are illustrated. The taxonomy for the model-\n
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text based techniques is shown in Fig. 2. The generative models are discussed in Sect. 2.\n
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text Section 3 discuss about the distance-based similarity model, discriminative models are\n
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text discussed in Sect. 4. The ontology-based models are discussed in Sect. 5, and deep\n
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text framework, and challenges in annotation models. The previously mentioned five\n
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text classifications of AVA strategies can be additionally arranged into a few sub-classes as\n
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text indicated by their fundamental ideas. Provides a taxonomy for Automatic Video\n
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text Annotation techniques.\n
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text Fig. 1. The process of Automatic Video Annotation\n
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text A State-of-Art Review on Automatic Video Annotation Techniques 1061\n
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text 2 Generative Model-Based Techniques\n
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text The generative model-based annotation techniques remain very prevalent, and good in\n
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text an early st 21 century. These techniques are committed to boost the probability of video\n
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text bilistic model of features extracted from video and labels to find the likelihood of a\n
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text video tag using training dataset. The generative models utilized for AVA comprise of\n
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text the relevance models. In probability and statistics, generative models characterize\n
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text arbitrarily generating recognizable information, regularly provides some unseen\n
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text observations. Generative models utilized machine learning for presenting data directly.\n
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text For the given input annotated training set, the authors in [2] have demonstrated\n
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text probabilistic models. It enables us to identify the likelihood of creating a tag from the\n
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text called as a double cross-media relevance model (DCMRM). It assesses the joint\n
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text probability through the accumulation of tags in a pre-characterized dictionary. It\n
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text includes two basic relations in video annotation. Namely, the word-to-video relation-\n
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text Outstanding contributions made by the generative models to the improvement of\n
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text annotation techniques. Nevertheless, there exist challenges in the generative models-\n
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text based strategies.\n
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text 1. Generative models are weak to optimize the tag prediction.\n
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text 2. Unable to recognize the complex semantic relationship.\n
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text 3. They require high computationally complex algorithms.\n
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text Fig. 2. Taxonomy for Automatic Video Annotation techniques.\n
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text 1062 K. Randive and R. Mohan\n
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text 3 Distance-Based Similarity Model-Based Techniques\n
", "default_label": "raw_text" }, "93": { "id": 93, "task_path": "images/000001_000093_img_bbox_txt_5108911bd628ad92134cf76f6e4d25b8_40016.jpg", "labeled": [ "raw_text" ], "data": { "location": { "page_number": 3, "bbox": { "x_top_left": 53, "y_top_left": 86, "width": 345, "height": 5 }, "name": "", "rotated_angle": 0.0 }, "order": 40016, "_line": "The distance-based similarity assumes videos with similar tags for annotation. For a\n", "_metadata": { "tag_hierarchy_level": { "level_1": null, "level_2": null, "can_be_multiline": false, "line_type": "unknown" }, "hierarchy_level": { "level_1": null, "level_2": null, "can_be_multiline": false, "line_type": "raw_text" }, "page_id": 3, "line_id": 40016 }, "_annotations": [ { "start": 0, "end": 85, "name": "indentation", "value": "53", "is_mergeable": true }, { "start": 0, "end": 85, "name": "spacing", "value": "18", "is_mergeable": true }, { "start": 0, "end": 3, "name": "size", "value": "9", "is_mergeable": true }, { "start": 0, "end": 3, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 4, "end": 18, "name": "size", "value": "9", "is_mergeable": true }, { "start": 4, "end": 18, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 19, "end": 29, "name": "size", "value": "9", "is_mergeable": true }, { "start": 19, "end": 29, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 30, "end": 37, "name": "size", "value": "9", "is_mergeable": true }, { "start": 30, "end": 37, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 38, "end": 44, "name": "size", "value": "9", "is_mergeable": true }, { "start": 38, "end": 44, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 45, "end": 49, "name": "size", "value": "9", "is_mergeable": true }, { "start": 45, "end": 49, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 50, "end": 57, "name": "size", "value": "9", "is_mergeable": true }, { "start": 50, "end": 57, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 58, "end": 62, "name": "size", "value": "9", "is_mergeable": true }, { "start": 58, "end": 62, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 63, "end": 66, "name": "size", "value": "9", "is_mergeable": true }, { "start": 63, "end": 66, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 67, "end": 78, "name": "size", "value": "9", "is_mergeable": true }, { "start": 67, "end": 78, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 79, "end": 82, "name": "size", "value": "9", "is_mergeable": true }, { "start": 79, "end": 82, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 83, "end": 84, "name": "size", "value": "9", "is_mergeable": true }, { "start": 83, "end": 84, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 0, "end": 85, "name": "bounding box", "value": "{\"x_top_left\": 0.12072892938496584, \"y_top_left\": 0.12912912912912913, \"width\": 0.785876993166287, \"height\": 0.0075075075075075074, \"page_width\": 439, \"page_height\": 666}", "is_mergeable": false } ], "_uid": "txt_5108911bd628ad92134cf76f6e4d25b8_40016", "original_document": "1708604654_915.pdf", "color": "#00f" }, "additional_info": "page_id 3
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text The distance-based similarity assumes videos with similar tags for annotation. For a\n
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text sample video, these techniques enable a search for a set of same videos, and the labels\n
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text of that video are depending on its similarity.\n
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text In [5], the authors proposed a beyond distance measurement to manage the defi-\n
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text ciency of the training data in video annotation by building neighborhood similarity. In\n
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text [6], the authors have presented an application of event detection from video range from\n
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text reducing the semantic gap. The inferred metric can be utilized in a CBVR system to\n
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text extract the semantically similar video. Nearest neighbor learning takes distance metric\n
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text from the given set of labeled points and marks the relation between distance in the\n
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text training data.\n
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text correlations to enhance the image and video annotation, at the same time classifies\n
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text concepts and represents them using a Correlative Multi-Label (CML) system [9].\n
", "default_label": "raw_text" }, "110": { "id": 110, "task_path": "images/000001_000110_img_bbox_txt_5108911bd628ad92134cf76f6e4d25b8_40222.jpg", "labeled": [ "raw_text" ], "data": { "location": { "page_number": 3, "bbox": { "x_top_left": 68, "y_top_left": 289, "width": 330, "height": 5 }, "name": "", "rotated_angle": 0.0 }, "order": 40222, "_line": "The Distance based similarity model-based AVA methods are structure-intuitive.\n", "_metadata": { "tag_hierarchy_level": { "level_1": null, "level_2": null, "can_be_multiline": false, "line_type": "unknown" }, "hierarchy_level": { "level_1": null, "level_2": null, "can_be_multiline": false, "line_type": "raw_text" }, "page_id": 3, "line_id": 40222 }, "_annotations": [ { "start": 0, "end": 79, "name": "indentation", "value": "68", "is_mergeable": true }, { "start": 0, "end": 79, "name": "spacing", "value": "6", "is_mergeable": true }, { "start": 0, "end": 3, "name": "size", "value": "9", "is_mergeable": true }, { "start": 0, "end": 3, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 4, "end": 12, "name": "size", "value": "9", "is_mergeable": true }, { "start": 4, "end": 12, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 13, "end": 18, "name": "size", "value": "9", "is_mergeable": true }, { "start": 13, "end": 18, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 19, "end": 29, "name": "size", "value": "9", "is_mergeable": true }, { "start": 19, "end": 29, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 30, "end": 41, "name": "size", "value": "9", "is_mergeable": true }, { "start": 30, "end": 41, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 42, "end": 45, "name": "size", "value": "9", "is_mergeable": true }, { "start": 42, "end": 45, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 46, "end": 53, "name": "size", "value": "9", "is_mergeable": true }, { "start": 46, "end": 53, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 54, "end": 57, "name": "size", "value": "9", "is_mergeable": true }, { "start": 54, "end": 57, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 58, "end": 78, "name": "size", "value": "9", "is_mergeable": true }, { "start": 58, "end": 78, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 0, "end": 79, "name": "bounding box", "value": "{\"x_top_left\": 0.1548974943052392, \"y_top_left\": 0.4339339339339339, \"width\": 0.7517084282460137, \"height\": 0.0075075075075075074, \"page_width\": 439, \"page_height\": 666}", "is_mergeable": false } ], "_uid": "txt_5108911bd628ad92134cf76f6e4d25b8_40222", "original_document": "1708604654_915.pdf", "color": "#00f" }, "additional_info": "page_id 3
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text The Distance based similarity model-based AVA methods are structure-intuitive.\n
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text Due to the high flexibility of the model, tag prediction is quite successful. However,\n
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text because of some intrinsic shortages, more improvements are expected.\n
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text 4 Discriminative Model-Based Techniques\n
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text Video annotation is considered as the multi-label classification problem in the dis-\n
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text criminative model-based annotation techniques. It is addressed with the use of binary\n
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text In [11], the authors have trained a target classifier using the web video sets that\n
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text adapt the pre-learned classifiers. This method is called as an adaptive latent structured\n
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text SVM. Here the locations of the key segment are considered as latent variables. In [14],\n
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text the authors presented the AVA technique, which is unsupervised leveraging video\n
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text content. This method uses the search procedure followed by mining. The video content\n
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text and the audio identified transcriptions are given as the queries, and the multimodal\n
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text optimized multigraph-based semi-supervised learning (OMG-SSL). The technique\n
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text tends to tackle different deciding aspects in the video annotation simultaneously.\n
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text Multiple methods and temporal consistency are included as deciding aspects. It\n
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text A State-of-Art Review on Automatic Video Annotation Techniques 1063\n
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text compares to the various relationship among video units and represented by different\n
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text graphs. A semi-supervised annotation approach, named as leaning an optimized graph\n
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text (OGL). The relationship between the data points can be embedded more accurately\n
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text from incomplete labels and multiple features in the proposed approach [17, 18]. In [19],\n
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text the authors proposed an approach for sentence generation from videos.\n
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text Despite the productivity and intuitionistic advantages of discriminative model-\n
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text based techniques, there exist challenges, which are as follows:\n
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text 1. The interrelationship among the visual features and tags is often excluded.\n
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text 2. These techniques are often are receptive to the quality of training datasets, because\n
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text of the predominant usage of label correlations.\n
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text The graph-based video techniques are transitive and can determine the labels of the\n
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text unannotated video. However, this is not useful in practical applications consisting of\n
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text the large video annotation tasks.\n
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text 5 Ontology-Based Techniques\n
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text Concept relationships are proven to be important learning assets that can improve the\n
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text effectiveness of video annotation. Ontology-based annotation techniques denote the\n
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text theoretical representations of the semantic relationship between the feature present in\n
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text video search engine was proposed. The combination of Boolean and temporal relations\n
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text for the automatic annotation and retrieval of video content is utilized by the ontologies\n
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text and the permitted queries. A framework for automatic semantic video annotation of\n
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text unconstrained videos is presented in [22]. This framework combines two layers,\n
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text namely annotation analysis using commonsense knowledgebase and low-level visual\n
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text similarity matching. Commonsense ontology is framed by consolidating multiple-\n
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text structured semantic relationships.\n
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text In [24], the authors formalized the method for analyzing the content in multimedia\n
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text using the interrelationship between the low- and high-level concept descriptors. In [25],\n
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text the authors demonstrated the fuzzy knowledge representations. The probability or the\n
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text degree of uncertainty is determined from these representations. The fuzzy Ontology\n
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text model populates the automatically generated ontology from the different video anno-\n
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text tation datasets. The content was utilized for the improvement of video semantic\n
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text interpretation.\n
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text In [26], the authors proposed a general block schematic for ontology-based video\n
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text annotation compared with different techniques In [27], the authors proposed a\n
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text 1064 K. Randive and R. Mohan\n
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text collaborative algorithm, which consensus-based social network analysis (SNA) for\n
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text utilizing an ontology-based approach. Simple semantics inferred from the ontology\n
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text works fine for the datasets consisting of a similar type of videos. However, they may\n
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text fail to annotate the dataset with different videos. So, tools that are more powerful are\n
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text required to learn semantics for substantial accumulations of videos with different\n
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text activities.\n
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text 6 Deep-Learning Model-Based Techniques\n
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text Recently, a feature representation for the video annotation is driven by deep learning-\n
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text based techniques because of the significant development in deep architectures. This\n
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text be used for extracting features that are robust [28–30, 35]. Deep learning systems are\n
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text Robust visual features are the primary component for video annotation. The con-\n
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text ventional handcrafted features are not reliable for complex concepts. The recent research\n
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text the authors proposed a multimodal feature learning mechanism based on Stacked\n
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text Contractive Autoencoder (SCAE) deep networks for automatic video annotation. In\n
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text [36], the proposed method is utilized to generate video representation with an emphasis\n
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text on temporal modeling namely Hierarchical Recurrent Neural Encoder (HRNE).\n
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text The CNN-RNN framework [40] uses Recurrent Neural Networks (RNN) with a\n
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text moderate level of computational complexity to identify high-order label relationships.\n
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text The framework uses convolutional and recurrent neural network mutually used for\n
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text determining the correlation between the adjacent labels and video representations. The\n
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text final outputs are computed based on the similarities among labels. In [37, 38], a\n
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text combinational deep neural networks based two-stream structure is proposed. The\n
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text structure is made out of two segments. The parallel two-stream encoding segment\n
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text which 3D CNN and takes video encoding from different sources. The input encoded\n
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text video representations are transferred using Long-Short-Term-Memory (LSTM)-based\n
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text decoding language to the annotations. In [39], the authors have proposed a hierarchical\n
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text LSTM with adjusted temporal attention (hLSTMat) approach for video annotation. In\n
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text [43], a framework is presented which simultaneously explores the learning of LSTM\n
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text and visual-semantic embedding (LSTM-E). In [44], a deep architecture is presented,\n
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text A State-of-Art Review on Automatic Video Annotation Techniques 1065\n
", "default_label": "raw_text" }, "222": { "id": 222, "task_path": "images/000001_000222_img_bbox_txt_5108911bd628ad92134cf76f6e4d25b8_70011.jpg", "labeled": [ "raw_text" ], "data": { "location": { "page_number": 6, "bbox": { "x_top_left": 39, "y_top_left": 60, "width": 345, "height": 5 }, "name": "", "rotated_angle": 0.0 }, "order": 70011, "_line": "which combines the transferred semantic attributes gained from images and video to\n", "_metadata": { "tag_hierarchy_level": { "level_1": null, "level_2": null, "can_be_multiline": false, "line_type": "unknown" }, "hierarchy_level": { "level_1": null, "level_2": null, "can_be_multiline": false, "line_type": "raw_text" }, "page_id": 6, "line_id": 70011 }, "_annotations": [ { "start": 0, "end": 83, "name": "indentation", "value": "39", "is_mergeable": true }, { "start": 0, "end": 83, "name": "spacing", "value": "0", "is_mergeable": true }, { "start": 0, "end": 5, "name": "size", "value": "9", "is_mergeable": true }, { "start": 0, "end": 5, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 6, "end": 14, "name": "size", "value": "9", "is_mergeable": true }, { "start": 6, "end": 14, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 15, "end": 18, "name": "size", "value": "9", "is_mergeable": true }, { "start": 15, "end": 18, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 19, "end": 30, "name": "size", "value": "9", "is_mergeable": true }, { "start": 19, "end": 30, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 31, "end": 39, "name": "size", "value": "9", "is_mergeable": true }, { "start": 31, "end": 39, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 40, "end": 50, "name": "size", "value": "9", "is_mergeable": true }, { "start": 40, "end": 50, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 51, "end": 57, "name": "size", "value": "9", "is_mergeable": true }, { "start": 51, "end": 57, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 58, "end": 62, "name": "size", "value": "9", "is_mergeable": true }, { "start": 58, "end": 62, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 63, "end": 69, "name": "size", "value": "9", "is_mergeable": true }, { "start": 63, "end": 69, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 70, "end": 73, "name": "size", "value": "9", "is_mergeable": true }, { "start": 70, "end": 73, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 74, "end": 79, "name": "size", "value": "9", "is_mergeable": true }, { "start": 74, "end": 79, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 80, "end": 82, "name": "size", "value": "9", "is_mergeable": true }, { "start": 80, "end": 82, "name": "style", "value": "OAMFJJ+AdvTT5ada87cc", "is_mergeable": true }, { "start": 0, "end": 83, "name": "bounding box", "value": "{\"x_top_left\": 0.0888382687927107, \"y_top_left\": 0.09009009009009009, \"width\": 0.785876993166287, \"height\": 0.0075075075075075074, \"page_width\": 439, \"page_height\": 666}", "is_mergeable": false } ], "_uid": "txt_5108911bd628ad92134cf76f6e4d25b8_70011", "original_document": "1708604654_915.pdf", "color": "#00f" }, "additional_info": "page_id 6
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text which combines the transferred semantic attributes gained from images and video to\n
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text the CNN plus RNN framework (LSTM-TSA).\n
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text It can be inferred that the deep learning-based annotation techniques have conveyed\n
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text both challenges and chances to AVA techniques. The late advancement and “leaps\n
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text forward” in deep learning assures to enhance the performance of video annotation on\n
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text large-scale video datasets. Yet there are two drawbacks present in the deep learning-\n
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text structure to decide the network structure leads to uncontrolled training phase involved\n
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text in the deep neural network.\n
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text 7 Discussion and Conclusions\n
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text In this paper, five types of AVA techniques are discussed regarding its framework,\n
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text merits, and challenges. The deep learning-based, the discriminative model-based, and\n
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text generative model-based annotation methods fall under the category of learning-based\n
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text techniques. In the discriminative model-based annotation technique, video annotation\n
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