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

WAN Shouhong; JIN Peiquan; XIA Yu; YUE Lihua

doi:10.1049/cje.2016.06.010

Volume 25 Issue 5

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WAN Shouhong, JIN Peiquan, XIA Yu, et al., “Incorporating Spatial Distribution Feature with Local Patterns for Content-Based Image Retrieval,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 873-879, 2016, doi: 10.1049/cje.2016.06.010

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WAN Shouhong, JIN Peiquan, XIA Yu, et al., “Incorporating Spatial Distribution Feature with Local Patterns for Content-Based Image Retrieval,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 873-879, 2016, doi: 10.1049/cje.2016.06.010

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

doi: 10.1049/cje.2016.06.010

Institute of Compute Science and Technology, University of Science and Technology of China, Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230027, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61272317), and the General Program of Natural Science Foundation of AnHui of China (No.1208085MF90).

Received Date: 2014-08-10
Rev Recd Date: 2015-01-20
Publish Date: 2016-09-10

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.
- Feature extraction or construction,
- Image retrieval,
- Feature representation

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References(17)

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