An automatic video annotation framework based on two level keyframe extraction mechanism

Aote, Shailendra S.; Potnurwar, Archana

doi:10.1007/s11042-018-6826-3

Cited by 21 publications

(20 citation statements)

References 31 publications

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“…Furthermore, Figure 14 illustrates that CLDTP is faster than vggNet for feature extraction. The comparison shows that our proposed descriptor provides better performance than the existing handcrafted algorithms: LBP, LTP, CLBP, LBPC [48], and DFT [8]. The existing descriptors only capture either the textural features or color features and ignore the shape information.…”

Section: Experimental Analysismentioning

confidence: 95%

“…In the spatial context annotation, researchers have exploited the natural property from each frame of a video. Visual context is assigned to each frame of a video in [7,8,[23][24][25][26][27][28][29]. Here the authors in [26] exploited the multi-level visual context from each frame of a video using the nearest neighbor approach.…”

Section: Related Workmentioning

confidence: 99%

“…After deriving the edge response (ER) for the neighbor (ER P ) pixels and center (ER C ) pixel, we use Equations (8) and (9) to calculate the ternary information, LDTP-TOP upper and LDTP-TOP lower respectively. Figure 7 illustrates the details of proposed LDTP-TOP algorithm.…”

Section: Local Directional Ternary Pattern-three Orthogonal Plane (Ldmentioning

confidence: 99%

“…To provide video annotation, several works have been presented [4][5][6][7][8]. Video annotation reduces the semantic gap between low-level features and high-level semantics.…”

Section: Introductionmentioning

confidence: 99%

“…The visual context in video annotation provides visual information that is relevant to the frame of a video. In [8][9][10] single or multiple tags/labels were annotated for a frame according to the relevant information. However, they did not consider semantic video information.…”

Section: Introductionmentioning

confidence: 99%

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A Distributed Automatic Video Annotation Platform

2020

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In the era of digital devices and the Internet, thousands of videos are taken and share through the Internet. Similarly, CCTV cameras in the digital city produce a large amount of video data that carry essential information. To handle the increased video data and generate knowledge, there is an increasing demand for distributed video annotation. Therefore, in this paper, we propose a novel distributed video annotation platform that explores the spatial information and temporal information. Afterward, we provide higher-level semantic information. The proposed framework is divided into two parts: spatial annotation and spatiotemporal annotation. Therefore, we propose a spatiotemporal descriptor, namely, volume local directional ternary pattern-three orthogonal planes (VLDTP–TOP) in a distributed manner using Spark. Moreover, we developed several state-of-the-art appearance-based and spatiotemporal-based feature descriptors on top of Spark. We also provide the distributed video annotation services for the end-users so that they can easily use the video annotation and APIs for development to produce new video annotation algorithms. Due to the lack of a spatiotemporal video annotation dataset that provides ground truth for both spatial and temporal information, we introduce a video annotation dataset, namely, STAD which provides ground truth for spatial and temporal information. An extensive experimental analysis was performed in order to validate the performance and scalability of the proposed feature descriptors, which proved the excellence of our proposed approach.

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Section: Experimental Analysismentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Section: Local Directional Ternary Pattern-three Orthogonal Plane (Ldmentioning

confidence: 99%

“…To provide video annotation, several works have been presented [4][5][6][7][8]. Video annotation reduces the semantic gap between low-level features and high-level semantics.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Distributed Automatic Video Annotation Platform

2020

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Extraction of Key Frame from Random Videos Based On Discrete Cosine Transformation

Gornale

Babaleshwar

Yannawar

2021

Communications in Computer and Information Science

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Study of Various Types of Data Annotation

Ningthoujam

Singh

2021

Advances in Intelligent Systems and Computing

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Labeling of digital data has made it easier for an algorithm to understand and process the dataset using machine learning techniques. There are various methods that are used to add the necessary information to gather data and achieve a perfect ground truth. The objective of this paper is to discuss the types of digital data annotation viz image, audio, and video. After discussing the various types, the paper focuses on different models used for annotating and how it has been evaluated on various dataset.

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An automatic video annotation framework based on two level keyframe extraction mechanism

Cited by 21 publications

References 31 publications

A Distributed Automatic Video Annotation Platform

A Distributed Automatic Video Annotation Platform

Extraction of Key Frame from Random Videos Based On Discrete Cosine Transformation

Study of Various Types of Data Annotation

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