Daxiang Li

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Aiming at the problem of object-based image retrieval, a novel semi-supervised multi-instance learning (MIL) algorithm based on RS (rough set) attribute reduction and Transductive support vector machine (TSVM) has been presented---RSTSVMMIL algorithm. This algorithm regards the whole image as a bag, and the low-level visual feature of the segmented regions(More)
SIFT features have been found to be effective in describing image textures. Because SIFT features have some great characteristics, such as translation invariance, zooming in and out invariance, spin invariance and affine invariance, etc, so the image retrieval precision is satisfactory usually. However, in Content Based Image Retrieval (CBIR), there are so(More)
In object matching and recognition it is useful to represent an image with the sets of local features such as SIFT etc. However this representation poses a challenge to the popular SVM machine learning method, since it needs ordered and fixlength data. To solve this problem, we focus in this paper on a Max-matching context kernel, which computes(More)
Focusing on the problem of natural image categorization, a novel multi-instance learning (MIL) algorithm based on rough set (RS) attribute reduction and support vector machine (SVM) is proposed. This algorithm regards each image as a bag, and lowlevel visual features of the segmented regions as instances. Firstly, a collection of "visual-words" is generated(More)
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