Recent advances in content based video copy detection

Shinde, Shantanu; Chiddarwar, G. G.

doi:10.1109/pervasive.2015.7087093

Cited by 13 publications

(4 citation statements)

References 24 publications

(46 reference statements)

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“…Methods like shot boundary detection (pixel comparison, transformation-based, histogram-based difference, edge, motion, statistical based), Shot change detection, Shot Classification Algorithm (Spatial feature, temporal domain of continuity metric) were discussed. Sanket Shinde et al [59] have proposed video detection approach base on SIFT feature. Then system is suitably robust to detect transformed versions of original videos by pointing the copied location using different photometric and geometric transformations.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Detection of Content based Video Retrieval

Gornale¹,

Babaleshwar²,

Babaleshwar³

2019

IJIGSP

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Content Based Video Retrieval (CBVR) System has been investigated over past decade it's rooted in many applications like developments and technologies. The demand for extraction of high level semantics contents as well as handling of low level contents in video retrieval systems are still in need. Hence it motivates and encourages many researchers to discover their knowledge across CBVR domain and contribute their work to make the system more effective and useful in developing the system application. This paper highlights comprehensive and extensive review of CBVR techniques for detection of region of interest in a given video. The experiment is carried out for the detection of ROI using ACF detector. The detection rate of ROI is observed competitive and satisfactory.

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Section: Related Workmentioning

confidence: 99%

Analysis and Detection of Content based Video Retrieval

Gornale¹,

Babaleshwar²,

Babaleshwar³

2019

IJIGSP

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“…Temporal information is usually compressed either by reducing the number of local features or by encoding multiple frames into a single global representation. Various visual search applications have emerged such as image-to-image retrieval, image-to-video retrieval, and video-to-video retrieval, including image-to-image retrieval (I2I) [11,12], videoto-video retrieval (V2V) [13,14], and image-to-video search (I2V) [15,16]. Speci cally, the well-known I2I visual search can be used for product search, in which relevant images are retrieved by the query image.…”

Section: -Introductionmentioning

confidence: 99%

An efficient approach for large-scale image-to-video retrieval with convolutional neural network features

Gharahsouflou

Maihami

Khamforoosh

2022

Preprint

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In recent years, inspired by the remarkable success in deep learning, many deep neural networks (DNN)-based methods have been proposed to solve the problem by intersection analysis. These approaches, for their enormous nonlinear learning capacity, show superior performance in cross-retrieval tasks compared to traditional cross-retrieval methods. Due to the increased amount of information for information recovery and limited storage capacity, the extraction of key-frame for videos and key feature extraction is becoming a fundamental challenge. In this paper, we have investigated the image-to-video retrieval challenge, and a new convolutional neural network (CNN)-based visual search system is presented to find similar videos in an extensive database based on the target image. Our framework includes a key frame extraction algorithm and feature aggregation strategy. Specifically, in the key frame extraction algorithm, by taking advantage of the extracted objects in each image, we present a new scheme to extract the features of each form. Therefore the redundant information in the video data will be removed, and the storage cost will also be reduced to an acceptable level. The feature aggregation strategy uses deep-local-complex feature fusion, which enables fast retrieval in a large-scale video database. The results of extensive experiments on publicly available datasets demonstrate that the proposed method achieves superior performance and accuracy compared to other advanced visual search methods.

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“…With a large amount of publicly available data, visual search has become an important frontier topic in the field of information retrieval. ere exist several kinds of visual search tasks, including image-to-image (I2I) search [1,2], video-to-video (V2V) search [3,4], and image-tovideo (I2V) search [5,6]. Specifically, the well-known I2I visual search can be used for product search, in which relevant images are retrieved by the query image.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Video Retrieval via Deep Local Convolutional Features

Zhang

Suo

et al. 2020

Advances in Multimedia

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In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

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Recent advances in content based video copy detection

Cited by 13 publications

References 24 publications

Analysis and Detection of Content based Video Retrieval

Analysis and Detection of Content based Video Retrieval

An efficient approach for large-scale image-to-video retrieval with convolutional neural network features

Large-Scale Video Retrieval via Deep Local Convolutional Features

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