Abstract
Developing low-dimensional discriminative features is crucial for content-based image retrieval (CBIR). In this paper, we present a square symmetrical local binary pattern (SSLBP) texture descriptor, which is a compact symmetrical-invariant variation of local binary pattern (LBP), then we propose a merging 2-class linear discriminant analysis (M2CLDA) method to capture low-dimensional optimal discriminative features in the projection space. M2CLDA calculates discriminant vectors with respect to each class in the one-vs.-all classification scenario and then merges all the discriminant vectors to form a projection matrix. The dimensionality of the M2CLDA space fits in with the number of classes involved. Our experiments show that the SSLBP feature is an effective variation of LBP, and the M2CLDA approach improves the performance of image retrieval and image classification observably as compared with the existed LDA approaches and takes less computation complexity than the kernel discriminant analysis (KDA) methods.
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