Open AccessIncorporating Spatial Distribution Feature with Local Patterns for Content‐Based Image Retrieval
Author(s) -
Wan Shouhong,
Jin Peiquan,
Xia Yu,
Yue Lihua
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.06.010
Subject(s) - feature (linguistics) , content (measure theory) , content based image retrieval , computer science , pattern recognition (psychology) , artificial intelligence , image (mathematics) , distribution (mathematics) , content distribution , image retrieval , computer vision , mathematics , philosophy , linguistics , mathematical analysis , computer network
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.