Incorporating Spatial Distribution Feature with Local Patterns for Content‐Based Image Retrieval. Issue 5 (1st September 2016)

• Record Type: Journal Article
• Authors: Wan, Shouhong; Jin, Peiquan; Xia, Yu; Yue, Lihua
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 5(2016)
• Page Start: 873
• Page End: 879
• Publication Date: 2016-09-01
• Subjects: Feature extraction or construction -- Image retrieval -- Feature representation; content‐based retrieval -- gradient methods -- image filtering -- image resolution -- image retrieval -- image texture; content‐based image retrieval -- gray‐level differences -- gray‐level variations -- LSDP patterns -- spatial distribution image feature -- CLSDP descriptor -- complete local spatial distribution pattern descriptor -- texture image feature -- filtered images -- gradient images; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.06.010
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.