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

作　　者：WAN Shouhong JIN Peiquan XIA Yu YUE Lihua 

机构地区：[1]Institute of Compute Science and Technology, University of Science and Technology of China, Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences

出　　处：《Chinese Journal of Electronics》2016年第5期873-879,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61272317);the General Program of Natural Science Foundation of AnHui of China(No.1208085MF90)

摘　　要：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.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.

关 键 词：Feature extraction or construction Image retrieval Feature representation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] P315.5[自动化与计算机技术—计算机科学与技术]