Abstract
The SIFT (Scale Invariant Feature Transform) feature is a descriptor of the key points in the gray-gradient spatial. It cannot distinguish samples with the same SIFT features and the varied color distributions. The paper proposes a novel feature expression, named HSH-SIFT, which firstly extracts the SIFT feature of Value channel in HSV image, secondly calculates the hue-saturation histogram (abbrev. HSH) of the neighborhood of the key point and flatten it to be a 96-D vector, finally attaches the HSH vector to the tail of the corresponding SIFT feature vector to construct a 224-D fusion feature vector. Secondly, it proposes a fusion match strategy to improve the precision of content-based image retrieval, it fuses k-D tree, NNDR strategy, unified perspective transformation, and symmetric test to filter mismatched feature points. Experiments have shown that it significantly effects the precision of ceramic image retrieval and has been applied to ceramic image retrieval application.
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