Multi-Scale Local Spatial Binary Patterns for Content-Based Image Retrieval
Content-based image retrieval (CBIR) has been widely studied in recent years. CBIR usually employs feature descriptors to describe the concerned characters of images, such as geometric descriptor and texture descriptor. Many texture descriptors in texture analysis and image retrieval are based on the so-called Local Binary Pattern (LBP) technique. However, LBP lacks of the spatial distribution information of texture features. In this paper, we aim at improving the traditional LBP and present a novel texture feature descriptor for CBIR called Multi-Scale Local Spatial Binary Patterns (MLSBP). MLSBP integrates LBP with spatial distribution information of gray-level variation direction and gray-level variation between the referenced pixel and its neighbors. In addition, MLSBP extracts the texture features from images on different scale levels. We conduct experiments to compare the performance of MLSBP with five competitors including LBP, Uniform LBP (ULBP), Completed LBP (CLBP), Local Ternary Patterns (LTP), and Local Tetra Patterns (LTrP). Also three benchmark image databases are used in the measurement, which are the Bradotz Texture Database (DB1), the MIT VisTex Database (DB2), and the Corel 1000 Database (DB3). The experimental results show that MLSBP is superior to the competitive algorithms in terms of precision and recall.
KeywordsContent-based image retrieval Local binary pattern Texture feature Spatial distribution
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