Segregated feedback with performance-based adaptive sampling for interactive news video retrieval

Luan, Huanbo; Neo, Shi-Yong; Goh, Hendra; Zhang, Yongdong; Lin, Sheng; Chua, Tat-Seng

doi:10.1145/1291233.1291295

Cited by 13 publications

(14 citation statements)

References 3 publications

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“…It utilizes an extensive annotation strategy for interactive search based on human reaction time. The VisionGo system [15]) supports multi-modal query and combines text derived from ASR, semantic concept classification, and low-level visual features and motion to rank videos. An active learning strategy is also used to maximize the users' interaction efforts.…”

Section: Query-by-concept Video Search Systemsmentioning

confidence: 99%

A consumer video search system by audio-visual concept classification

Jiang

Loui

Lei

2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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Indexing and searching the massive amount of consumer videos in the open domain is increasingly important. Dueto the lack of text descriptions as well as the difficulties in analyzing the content of consumer videos, little work has been conducted to provide video search engines in the consumer domain. In this paper, we develop a contentbased consumer video search system based on multi-modal concept classification. The system supports the query-byexample access mechanism, by exploiting the query-byconcept search paradigm underneath, where online concept classification is conducted over the query video by integrating both visual and audio information. The system adopts an audio-visual grouplet representation that captures salient audio-visual signatures to describe the video content for efficient concept classification. Experiments over the large-scale Columbia Consumer Video set show the effectiveness of the developed system.

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Section: Query-by-concept Video Search Systemsmentioning

confidence: 99%

A consumer video search system by audio-visual concept classification

Jiang

Loui

Lei

2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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“…For example, Luan et al [269] iteratively select videos that are the most relevant to the query until the number of videos labeled as relevant by users in an iteration step becomes very small. Then, the videos closest to the classifier boundary are returned to users for identification and the system is updated using the identified videos.…”

Section: ) Explicit Relevance Feedbackmentioning

confidence: 99%

A Survey on Visual Content-Based Video Indexing and Retrieval

Xie

et al. 2011

IEEE Trans. Syst., Man, Cybern. C

471

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Abstract-Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.Index Terms-Feature extraction, video annotation, video browsing, video retrieval, video structure analysis.

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“…One problem concerning the usage of too many visual features is the curse of high dimensionality, which may degrade performance and introduce large time cost. In order to cater to real-time retrieval, we restrict the feature size to a 116-feature vector for each key frame, consisting of 27-dimension color moment features (including 1st, 2nd, and 3rd moments) obtained at a 3 Â 3 block, 80-dimension normalized local edge histogram texture feature, eight directional motion features and one global motion feature [25].…”

Section: Low-level Visual Featuresmentioning

confidence: 99%