An integrated system for content-based video retrieval and browsing

Zhang, Hong Jiang; Wu, Jian; Zhong, Dexing; Smoliar, Stephen W.

doi:10.1016/s0031-3203(96)00109-4

Cited by 426 publications

(223 citation statements)

References 7 publications

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“…Therefore, methods were needed to extract key frames compliant to visual content variations along a video sequence. One of the first key-frame extraction approaches developed in view of this objective is presented in [22], with all details given in [25]. There, key frames are extracted in a sequential fashion for each shot separately.…”

Section: A Key-frame Extractionmentioning

confidence: 99%

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

Hanjalic

Zhang

1999

IEEE Trans. Circuits Syst. Video Technol.

286

115

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Abstract-Key frames and previews are two forms of a video abstract, widely used for various applications in video browsing and retrieval systems. We propose in this paper a novel method for generating these two abstract forms for an arbitrary video sequence. The underlying principle of the proposed method is the removal of the visual-content redundancy among video frames. This is done by first applying multiple partitional clustering to all frames of a video sequence and then selecting the most suitable clustering option(s) using an unsupervised procedure for clustervalidity analysis. In the last step, key frames are selected as centroids of obtained optimal clusters. Video shots, to which key frames belong, are concatenated to form the preview sequence.Index Terms-Clustering, cluster-validity analysis, contentbased video retrieval, content classification, video content analysis.

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Section: A Key-frame Extractionmentioning

confidence: 99%

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

Hanjalic

Zhang

1999

IEEE Trans. Circuits Syst. Video Technol.

286

115

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“…Video summarization can shorten video in several ways. In this paper, we focus on the two most common ones: keyframe selection, where the system identifies a series of defining frames [1,2,3,4,5] and key subshot selection, where the system identifies a series of defining subshots, each of which is a temporally contiguous set of frames spanning a short time interval [6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Video Summarization with Long Short-Term Memory

Zhang

Chao

Sha

et al. 2016

Lecture Notes in Computer Science

533

696

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Abstract. We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the task as a structured prediction problem, our main idea is to use Long Short-Term Memory (LSTM) to model the variable-range temporal dependency among video frames, so as to derive both representative and compact video summaries. The proposed model successfully accounts for the sequential structure crucial to generating meaningful video summaries, leading to state-of-the-art results on two benchmark datasets. In addition to advances in modeling techniques, we introduce a strategy to address the need for a large amount of annotated data for training complex learning approaches to summarization. There, our main idea is to exploit auxiliary annotated video summarization datasets, in spite of their heterogeneity in visual styles and contents. Specifically, we show that domain adaptation techniques can improve learning by reducing the discrepancies in the original datasets' statistical properties.

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“…Furthermore, the first frame normally is not stable and does not capture the major visual content. In other proposed approach (Zhang et al, 1997), the current frame of the shot will be compared against the last key frame. If significant content change occurs, the current frame will be selected as a new key frame, note that first frame will be selected as the first key frame.…”

Section: Related Work On Key Frame Extractionmentioning

confidence: 99%

SVM-based Relevance Feedback for semantic video retrieval

Yazdi

Javidi

Pourreza

2009

IJSISE

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This paper presents a novel method for efficient key frame extraction from video shot representation and employs a Support-Vector-Machine-based Relevance Feedback (SVM-RF) to bridging semantic gap between low-level feature and high-level concepts of shots. We introduce a new approach for key frame extraction using a hierarchical approach based on clustering. Using this key frame representation, the most representative key frame is then selected for each shot. Furthermore, our system incorporates user to judge about the result of retrieval and labelled retrieved shot in two groups, relevant and irrelevant. Then, by mean feature of relevant and irrelevant shots train an SVM classifier. In the next step, video database is classified in two groups, relevant and irrelevant shots. Suitable Graphic User Interface (GUI) is shown for capturing RF of user. This process continued until user satisfied with results. The proposed system is checked over collected shots from Trecvid2001 database and home videos include 800 shots of different concepts (10 semantic groups). Experimental results demonstrate the effectiveness of the proposed method.

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An integrated system for content-based video retrieval and browsing

Cited by 426 publications

References 7 publications

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

Video Summarization with Long Short-Term Memory

SVM-based Relevance Feedback for semantic video retrieval

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