Abstract
Aiming at the problems of high dimensional features, poor viewpoint robustness and long retrieval time of existing algorithms in the image retrieval system, this paper presents a new image retrieval algorithm by integrating image color information and surface geometry principal curvatures information. In the proposed method, the color image is first quantized and counted to obtain its color histogram. Simultaneously, the Hessian matrix is used to extract the texture information and the joint histogram of oriented gradient with mix-sampling and multi-scale is constructed. And then, the obtained color histogram and histogram of oriented gradient are fused to obtain the final joint histogram. Experiments are performed on public datasets, and comparison and analysis with representative algorithms based on a single visual feature or set of visual features to verify the performance of our algorithm. The experimental results show that the proposed method has the advantages of low dimensionality, fastness, strong viewpoint robustness and high precision, and can realize image retrieval efficiently.
Highlights
With the application of computer vision and digital devices, image processing is more and more widely used in medical images [1], [2], image super-resolution [3], [4], remote sensing images [5], and other fields such as social applications [6], [7]
By using image retrieval technology, the geographic image information captured by the satellite can be found . what’s more, image retrieval technology can be used for skin detection of pedestrians with high accuracy [8]
By using all components of color space Hor et al [29] presented a new image retrieval method which is generated by two different texture descriptors and used the two descriptors to extract texture information with high accuracy
Summary
With the application of computer vision and digital devices, image processing is more and more widely used in medical images [1], [2], image super-resolution [3], [4], remote sensing images [5], and other fields such as social applications [6], [7]. By using all components of color space Hor et al [29] presented a new image retrieval method which is generated by two different texture descriptors and used the two descriptors to extract texture information with high accuracy. By combining local and global features, Bani and Fekri-Ershad [30] proposed a new algorithm In this approach, the quantized color histogram was employed to extract the global color information in spatial domain. Ahmed et al [34] proposed a new method which combined the local image features, spatial information in BoW architecture for image retrieval This approach used keypoints detection strategy to improve precision. This paper proposed a new image retrieval algorithm called F-MHOGs that fuses the color information and geometric structure with mix-sampling and multi-scale principal curvatures(PCs) of the image.
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