Abstract
As a broad area of field and applications, texture analysis from the area of remote sensing to biomedical imaging, image inpainting, etc., for each area of these, it requires some raw data image to extract some meaningful features that define the characteristics of an image. In this manner, the content-based image retrieval (CBIR) plays a vital role in finding the similarity of images to query image. Either number of work had been done in these directions, but there is still the scope of work to be done. With the continuation on this, analysing the different research papers on texture-based feature extraction especially on local binary pattern (LBP) and local phase quantization (LPQ), here this paper tries to analyse and compare the efficiency of image retrieval with respect to their texture feature vector dataset of images with the help of different texture feature extraction techniques, especially LBP and LPQ. This paper is focused on the LBP and LPQ techniques and its comparative analysis.
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Dolly, B., Raj, D. (2021). Image Retrieval Based on Texture Using Local Binary Pattern and Local Phase Quantization. In: Singh Mer, K.K., Semwal, V.B., Bijalwan, V., Crespo, R.G. (eds) Proceedings of Integrated Intelligence Enable Networks and Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6307-6_7
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DOI: https://doi.org/10.1007/978-981-33-6307-6_7
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