Abstract
This paper proposes a novel image retrieval model based on monogenic signal representation. An original image is decomposed into three complementary components: amplitude, orientation and phase by monogenic signal representation. The monogenic variation in each local region and monogenic feature in each pixel are encoded, and then the statistical features of the local features encoded are calculated. In order to overcome the problem of high feature dimensionality, the local statistical features extracted from the complementary monogenic components are projected by block-based fisher discriminant analysis, which not only reduces the dimensionality of the features extracted, but also enhances its discriminative power. Finally, these features reduced are fused for effective image retrieval. Experimental results show that our scheme can effectively describe an image, and obviously improve the average retrieval precision.
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