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    National Tsing Hua University Institutional Repository > 電機資訊學院 > 電機工程學系 > 期刊論文 >  Saliency detection in the compressed domain for adaptive image retargeting


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83629


    Title: Saliency detection in the compressed domain for adaptive image retargeting
    Authors: Yuming Fang;Weisi Lin;Zhenzhong Chen;Chia-Wen Lin
    教師: 林嘉文
    Date: 2012
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: IEEE TRANSACTIONS ON IMAGE PROCESSING, Institute of Electrical and Electronics Engineers, Volume 21, Issue 9, SEP 2012, Pages 3888-3901
    Keywords: VISUAL-ATTENTION
    MODEL
    COLOR
    HOMOGENEITY
    TEXTURE
    SEGMENTATION
    VIDEO
    Abstract: Saliency detection plays important roles in many image processing applications, such as regions of interest extraction and image resizing. Existing saliency detection models are built in the uncompressed domain. Since most images over Internet are typically stored in the compressed domain such as joint photographic experts group (JPEG), we propose a novel saliency detection model in the compressed domain in this paper. The intensity, color, and texture features of the image are extracted from discrete cosine transform (DCT) coefficients in the JPEG bit-stream. Saliency value of each DCT block is obtained based on the Hausdorff distance calculation and feature map fusion. Based on the proposed saliency detection model, we further design an adaptive image retargeting algorithm in the compressed domain. The proposed image retargeting algorithm utilizes multioperator operation comprised of the block-based seam carving and the image scaling to resize images. A new definition of texture homogeneity is given to determine the amount of removal block-based seams. Thanks to the directly derived accurate saliency information from the compressed domain, the proposed image retargeting algorithm effectively preserves the visually important regions for images, efficiently removes the less crucial regions, and therefore significantly outperforms the relevant state-of-the-art algorithms, as demonstrated with the in-depth analysis in the extensive experiments.
    Relation Link: http:/dx.doi.org/10.1109/TIP.2012.2199126
    http://www.ieee.org/
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/83629
    Appears in Collections:[電機工程學系] 期刊論文
    [光電研究中心] 期刊論文

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