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Journal ArticleDOI

A novel block intensity comparison code for video classification and retrieval

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TLDR
It is found that, BICC outperforms other visual features such as edge, motion and histogram which are commonly used for video classification, and reduces the redundancy while exploiting the correlations between the feature elements.
Abstract
Appropriate organization of video databases is essential for pertinent indexing and retrieval of visual information. This paper proposes a new feature called block intensity comparison code (BICC) for video classification and retrieval. Block intensity comparison code represents the average block intensity difference between blocks of a frame. The extracted feature is further processed using principal component analysis (PCA) to reduce the redundancy while exploiting the correlations between the feature elements. The temporal nature of video is modeled by hidden Markov model (HMM) with BICC as the features. It is found that, BICC outperforms other visual features such as edge, motion and histogram which are commonly used for video classification.

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Citations
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References
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Journal ArticleDOI

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TL;DR: Adaptive Control Processes: A Guided Tour as mentioned in this paper is a guidebook for guided tours of control processes, with a focus on adaptive control processes, and a description of the tour.
Journal ArticleDOI

Multimedia content analysis-using both audio and visual clues

TL;DR: This work describes audio and visual features that can effectively characterize scene content, present selected algorithms for segmentation and classification, and review some testbed systems for video archiving and retrieval.
Journal ArticleDOI

A Survey on the Automatic Indexing of Video Data

TL;DR: This paper surveys several approaches and algorithms that have been recently proposed to automatically structure audio?visual data, both for annotation and access.
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