Incorporating Spatial Distribution Feature with Local Patterns for Content-Based Image Retrieval

Local patterns record the gray-level differences between a referenced pixel in an image and its surrounding pixels, which have been commonly used to describe the image features. However, traditional local patterns ignore the spatial distribution feature of texture information in images. We group the gray-level variations along three directions, i.e., horizontal, vertical, and diagonal directions. Each group is then merged into a Local spatial distribution pattern(LSDP) to represent the spatial distribution image feature. We also construct the LSDP patterns for gradient and filtered images, and finally form the Complete local spatial distribution pattern(CLSDP)descriptor to completely describe the texture image feature. Experiments on textural and natural image sets were conducted to compare our CLSDP-based image retrieval algorithm with four previous competitors. The results show that our method is superior to existing algorithms considering both average precision and recall.