Abstract
Image proceeding is foundation for pattern recognition and machine learning. The recent research focuses on image retrieval techniques with machine and deep learning methodologies. This paper recommends an other approach for extracting images based on combined characteristics of color and texture with edge binary pattern technique. The suggested descriptor first translates an RGB image into HSV color space. In this approach, the HSV color model makes use of the fundamental properties of color image as color, intensity, and brightness of an image. The hue (H) and saturation (S) components support to extract the color features, and the value component is more feasible to obtain the texture features; upon the value constituent of each image, local maximum edge binary patterns (LMEBPs) are used to find the relations among the pixels for every 3 × 3 matrix in order to extract texture features. Finally, all three histograms were used to extract the feature vector. The presented algorithm is experimented on two well-known color databases, Corel-10k and MIT-Visex. The comparative analysis of the proposed approach in terms of retrieval performance, with existing methods like CS-LBP, LEPSEG, LEPINV, has shown a substantial perfection in terms of precision and recall.
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Sucharitha, G., Kalyani, B.J.D., Chandra Sekhar, G., Srividya, C. (2023). Efficient Image Retrieval Technique with Local Edge Binary Pattern Using Combined Color and Texture Features. In: Chatterjee, P., Pamucar, D., Yazdani, M., Panchal, D. (eds) Computational Intelligence for Engineering and Management Applications. Lecture Notes in Electrical Engineering, vol 984. Springer, Singapore. https://doi.org/10.1007/978-981-19-8493-8_21
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