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Browsing by Author "Fu, Hsin Chia"

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    A multiple-instance neural networks based image content retrieval system
    (2006)
    Chuang, Shun-Chin
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    Xu, Yeong-Yuh
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    Fu, Hsin Chia
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    Huang, Hsiang-Cheh
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    資訊工程學系
    In this paper, we proposed a Multiple-Instance Neural Network (MINN) for content-based image retrieval (CBIR). In order to represent the rich content of an image without precisely image segmentation, the image retrieval problem is considered as a multiple-instance learning problem. A set of exemplar images are selected by a user, each of which is labelled as conceptual related (positive) or conceptual unrelated (negative) image. Then, the proposed MINN is trained by using the proposed learning algorithm to learn the user's preferred image concept from the positive and negative examples. Experimental results show that: (1) without image segmentation and using only the color histogram as the image feature, the MINN without relevance feedback performs slightly inferior to some leading image retrieval methods, and (2) the MINN with the relevance feedback can significantly improve the retrieving performance from 40.3% to 59.3%, which outperforms to the results of some leading image retrieval methods.
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    OBJECT-BASED IMAGE SEARCH SYSTEM AND METHOD
    (2007-12-13)
    Fu, Hsin Chia
    ;
    Xu, Yeong Yuh
    The present invention discloses an object-based image search system and method, whereby a user may visually specify query objects for retrieving relevant images without using any image-segmenting software tool. The method of the present invention comprises: specifying target feature points from sample images which are displayed in an image query interface and determining logic relationships of the target feature points; utilizing a feature-point checking program to correlate the target feature points with target objects; utilizing a feature-similarity calculating program to find out the images relevant to the target objects via the image feature parameters stored in a feature database; arranging and presenting the relevant images in sequence.