Improving image retrieval by integrating shape and texture features


Content-based image retrieval (CBIR) has been an active research topic in the last decade. Multiple feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a new CBIR method based on an efficient integration of texture and shape features. The texture features are extracted on the decomposed images processed by the optimal non-subsampled shearlet transform (NSST), and are represented by the high-frequency sub-band coefficients, which can be modeled by Bessel K Form (BKF) distribution; the shape features are represented by low-order quaternion polar harmonic transforms (QPHTs). The two kinds of features are then integrated by a weighted distance measurement, where Kullback-Leibler distance (KLD) and Euclidean distance (ED) are used for texture and shape features respectively. The integration of shape and texture information provides a robust feature set for image retrieval. Experimental results on standard benchmarks show significant improvements on retrieval performance using the proposed method compared with previous state-of-the-art methods.

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This work was partially supported by the National Science Fund of China under Grant Nos. 61702262, 61602226,U1713208 and 61472187, the 973 Program No. 2014CB349303, Program for Changjiang Scholars, and “the Fundamental Research Funds for the Central Universities” No. 30918011322.

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Correspondence to Yu-Nan Liu.

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Liu, Y., Zhang, S., Sang, Y. et al. Improving image retrieval by integrating shape and texture features. Multimed Tools Appl 78, 2525–2550 (2019).

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  • Content-based image retrieval
  • Non-subsampled shearlet transform
  • BKF distribution
  • Quaternion polar harmonic transforms