Deep Learning Based Semantic Video Indexing and Retrieval

Podlesnaya, Anna; Podlesnyy, Sergey

doi:10.1007/978-3-319-56991-8_27

Cited by 18 publications

(6 citation statements)

References 14 publications

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“…Porter et al [64], depicted a shot by a directed weighted graph. Motivated by ImageNet, Podlesnaya et al [8], introduced a way of building an index that is oriented by a graph that the nodes are the salient objects and edges are linked to other objects or the WordNet lexical database and used a Neo4j graph-oriented database.…”

Section: Hierarchicalmentioning

confidence: 99%

Video semantics exploration for indexing and retrieval

2020

Al-Azhar Bulletin of Science

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Video semantic concepts exploration is a fundamental problem in a video indexing and retrieval. It has a long history of investigation since early days till recent achievements. The challenges lie in bridging the gap between low level features and semantic level ones. To stand on the thoroughly situation, video semantic concepts exploration for indexing and retrieval purposes evolution from conventional methods to the state-ofthe-art ones will be reviewed. The main contribution is to unify concepts involved and evolution in this interesting topic.

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Section: Hierarchicalmentioning

confidence: 99%

Video semantics exploration for indexing and retrieval

2020

Al-Azhar Bulletin of Science

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“…Due to the great advantages of deep learning technology in multimedia retrieval, such as Wu et al (2018e), more and more researchers utilize deep neural networks such as CNN for the task of video retrieval, such as Wu et al (2018b). Podlesnaya and Podlesnyy (2016) utilized CNN to extract visual features of videos, which serve as universal signature for retrieval tasks. For the problem of near-duplicate video retrieval which is one of the types of video to video problems, Kordopatis-Zilos et al (2017) a novel scheme by using CNN features from intermediate layers to create discerning global video representations integrating with a deep metric learning framework.…”

Section: Related Workmentioning

confidence: 99%

CNN-VWII: An efficient approach for large-scale video retrieval by image queries

Zhang

Lin

Zhu

et al. 2019

Pattern Recognition Letters

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This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-k image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we proposed a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the state-of-the-art techniques with similar accuracy.

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“…Kuo et al [4] presented the work on the use of deep convolutional neural network for image retrieval. Podlesnaya and Podlesnyy [5] and [6] exhibits the work on deep learning based video indexing and retrieval. Kikuchi et al [7] presented the work on video semantic indexing using object detection-derived features.…”

Section: Related Workmentioning

confidence: 99%

Semantic concept based video retrieval using convolutional neural network

Janwe¹,

Bhoyar

2019

SN Appl. Sci.

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Retrieval of videos efficiently and effectively has become a challenging issue nowadays and dealing with multi-concept videos is the center of focus. The aim of the work presented here is to propose an improved semantic concept-based video retrieval method using a novel ranked intersection filtering technique and a foreground driven concept co-occurrence matrix. In the proposed ranked intersection filtering technique, an intersection of ranked concept probability scores is taken from key-frames associated with a query shot to identify concepts to be used in retrieval. Convolutional neural network is used as a baseline. The proposed method is implemented using a classifier built with a fusion of asymmetrically trained deep CNNs to deal with data imbalance problem, a novel foreground driven concept co-occurrence matrix to exploit concept co-occurrence information and a ranked intersection filtering approach. Performance is evaluated by a measure, mean average precision on TRECVID multi-label dataset. The results are compared with state-of-the-art other existing methods in its class and shown its superiority.

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Deep Learning Based Semantic Video Indexing and Retrieval

Cited by 18 publications

References 14 publications

Video semantics exploration for indexing and retrieval

Video semantics exploration for indexing and retrieval

CNN-VWII: An efficient approach for large-scale video retrieval by image queries

Semantic concept based video retrieval using convolutional neural network

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