The Experts below are selected from a list of 116721 Experts worldwide ranked by ideXlab platform

Henry Chang - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Topic-sensitive interactive image object retrieval with noise-proof relevance feedback
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Jen-hao Hsiao, Henry Chang
    Abstract:

    One current direction to enhance the Search accuracy in visual object retrieval is to reformulate the original query through (pseudo-)relevance feedback, which augments a query with visual terms from the image documents most highly ranked by an Initial Search or identified by user. However, query and feedback images usually contain multiple objects or aspects, and as a consequence the original query's focus may drift because of the newly added terms and noises. The results of using an augmented query are thus often inferior to that of using only the original one. In this paper we propose the topic-sensitive image retrieval with noise-proof relevance feedback to address the query drift problem in visual object retrieval. The proposed method removes irrelevant noises and topics from both query and feedback images to prevent query drift. A discriminative learning strategy is then employed to re-rank and improve the Initial Search result. Experiments on a real world data set demonstrate the effectiveness of our approach and show that the proposed approach can better learn user intention.

Mubarak Shah - One of the best experts on this subject based on the ideXlab platform.

  • ICME - Automatic Query Expansion for News Video Retrieval
    2006 IEEE International Conference on Multimedia and Expo, 2006
    Co-Authors: Yun Zhai, Jingen Liu, Mubarak Shah
    Abstract:

    In this paper, we present an integrated system for news video retrieval. The proposed system incorporates both speech and visual information in the Search mechanisms. The Initial Search is based on the automatic speech recognition (ASR) transcript of video. Based on the relevant shots selected from the Initial Search round, keyword histograms are automatically generated for the refinement of the Search query, such that the reformulated query fits better to the target topic. We have also developed an image-based refinement module, which uses the region analysis of the video key-frames. SR-tree like indexing structure is constructed for the region features, and the image-to-image similarity is computed using the Earth Mover's Distance. By performing a series of relevance feedback processes, the set of the true relevant shots is expanded significantly. The proposed system has been applied to a large open-benchmark news video dataset, and very satisfactory improvements have been obtained by applying the proposed automatic query expansion and the region-based refinement.

Makai-kuang - One of the best experts on this subject based on the ideXlab platform.

Jen-hao Hsiao - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Topic-sensitive interactive image object retrieval with noise-proof relevance feedback
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Jen-hao Hsiao, Henry Chang
    Abstract:

    One current direction to enhance the Search accuracy in visual object retrieval is to reformulate the original query through (pseudo-)relevance feedback, which augments a query with visual terms from the image documents most highly ranked by an Initial Search or identified by user. However, query and feedback images usually contain multiple objects or aspects, and as a consequence the original query's focus may drift because of the newly added terms and noises. The results of using an augmented query are thus often inferior to that of using only the original one. In this paper we propose the topic-sensitive image retrieval with noise-proof relevance feedback to address the query drift problem in visual object retrieval. The proposed method removes irrelevant noises and topics from both query and feedback images to prevent query drift. A discriminative learning strategy is then employed to re-rank and improve the Initial Search result. Experiments on a real world data set demonstrate the effectiveness of our approach and show that the proposed approach can better learn user intention.

Yun Zhai - One of the best experts on this subject based on the ideXlab platform.

  • ICME - Automatic Query Expansion for News Video Retrieval
    2006 IEEE International Conference on Multimedia and Expo, 2006
    Co-Authors: Yun Zhai, Jingen Liu, Mubarak Shah
    Abstract:

    In this paper, we present an integrated system for news video retrieval. The proposed system incorporates both speech and visual information in the Search mechanisms. The Initial Search is based on the automatic speech recognition (ASR) transcript of video. Based on the relevant shots selected from the Initial Search round, keyword histograms are automatically generated for the refinement of the Search query, such that the reformulated query fits better to the target topic. We have also developed an image-based refinement module, which uses the region analysis of the video key-frames. SR-tree like indexing structure is constructed for the region features, and the image-to-image similarity is computed using the Earth Mover's Distance. By performing a series of relevance feedback processes, the set of the true relevant shots is expanded significantly. The proposed system has been applied to a large open-benchmark news video dataset, and very satisfactory improvements have been obtained by applying the proposed automatic query expansion and the region-based refinement.