Abstract
With the rapid development of Internet technology, the number of images has shown explosive growth. Content-based image retrieval is an important research topic in the field of computer vision, and it aims to efficiently retrieve target images in massive image databases. Due to the different image characteristics in various fields, the previous image retrieval based on single content feature (color, shape, texture, etc.) can no longer meet the application requirements of image retrieval in related fields. In order to efficiently retrieve specific target images in related fields, based on the typical image content feature namely the color moments, image hash feature including perceptual hash, average hash, and difference hash was respectively fused with the color moments to retrieve images in this paper. Aiming to improve the retrieval efficiency, a weighted lightweight image retrieval method based on linear regression was proposed. Linear regression analysis was performed on image perceptual hash, average hash and color moments. The similarity obtained by the faster hash feature was used to replace the color moments. Finally, the hash feature was merged with the new color moments to retrieve the image. Experimental results show that compared with the direct fusion of image hash feature and color moments, the weighted lightweight image retrieval method based on linear regression proposed in this paper can improve the retrieval efficiency while maintaining the retrieval accuracy.
Supported by Natural Science Basic Research Program of Shaanxi(No.2017JQ6026) and Yulin Science and Technology Plan Production-University-Research Program(No.2014CXY-08-01).
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Zhang, L., Zheng, X., Dang, X., Zhang, J. (2020). Weighted Lightweight Image Retrieval Method Based on Linear Regression. In: Ben Hedia, B., Chen, YF., Liu, G., Yu, Z. (eds) Verification and Evaluation of Computer and Communication Systems. VECoS 2020. Lecture Notes in Computer Science(), vol 12519. Springer, Cham. https://doi.org/10.1007/978-3-030-65955-4_20
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