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