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Content-based image retrieval via a hierarchical-local-feature extraction scheme


Recently, with the development of various camera sensors and internet network, the volume of digital images is becoming big. Content-based image retrieval (CBIR), especially in network big data analysis, has attracted wide attention. CBIR systems normally search the most similar images to the given query example among a wide range of candidate images. However, human psychology suggests that users concern more about regions of their interest and merely want to retrieve images containing relevant regions, while ignoring irrelevant image areas (such as the texture regions or background). Previous CBIR system on user-interested image retrieval generally requires complicated segmentation of the region from the background. In this paper, we propose a novel hierarchical-local-feature extraction scheme for CBIR, whereas complex image segmentation is avoided. In our CBIR system, a perception-based directional patch extraction method and an improved salient patch detection algorithm are proposed for local features extraction. Then, color moments and Gabor texture features are employed to index the salient regions. Extensive experiments have been performed to evaluate the performance of the proposed scheme, and experimental results show that the developed CBIR system produces plausible retrieval results.

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This work was supported by National Natural Science Foundation of China (NSFC) (61601427, 61602229, 61771230); Natural Science Foundation of Shandong Province (ZR2015FQ011, ZR2016FM40); Shandong Provincial Key Research and Development Program of China (NO. 2017CXGC0701); Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions.

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Correspondence to Muwei Jian.

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Jian, M., Yin, Y., Dong, J. et al. Content-based image retrieval via a hierarchical-local-feature extraction scheme. Multimed Tools Appl 77, 29099–29117 (2018).

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  • Network big data
  • Content-based image retrieval
  • Perception-based directional patch
  • Salient patch detection