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An Efficient Video Retrieval Algorithm Using Key Frame Matching for Video Content Management

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초록

To manipulate large video contents, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-wise user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm that extracts key frames using color histograms and matches the video sequences using edge features. To effectively match video sequences with a low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with real sequence show that the proposed video sequence matching algorithm using edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

Abstract

To manipulate large video contents, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-wise user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm that extracts key frames using color histograms and matches the video sequences using edge features. To effectively match video sequences with a low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with real sequence show that the proposed video sequence matching algorithm using edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

참고문헌 (10)

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  6. J. Geng, "Efficient heuristic methods for multimodal fusion and concept fusion in video concept detection", IEEE Trans. Multimedia, 17(4), pp.498~511 (2015)
  7. H Yan, "Image-based process monitoring using low-rank tensor decomposition", IEEE Trans. Automation Science and Engineering, 12(1), pp.216~227 (2015)
  8. Y. Yin, "On generating content-oriented geo features for sensor-rich outdoor video search", IEEE Trans. Multimedia, 17(10), pp.1760~1772 (2015)
  9. S. Youm, "RFID-based automatic scoring system for physical fitness testing", IEEE Systems, 9(2), pp.326~334 (2015)
  10. S. Ferdousi, "Disaster-aware datacenter placement and dynamic content management in cloud networks", IEEE Optical Communication and Networking, 7(7), pp.681~694 (2015)

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