Abstract
Texture analysis is critical in a variety of computer vision applications, including object recognition, defect detection on surfaces, pattern recognition, and medical picture analysis. The purpose of this research is to offer a novel method for content-based texture picture classification that is based on the discrete wavelet transformation and several texture properties. Three approaches (LBP, DWT, and Tamura) are combined to build an efficient hybrid function vector capable of extracting the finest texture information. The study extracts LBP and Tamura features in two methods, via wavelet transform and fusion, to create an effective hybrid texture feature vector. Experiments on the Brodatz and MIT-VisTex databases demonstrate that the proposed approach is more precise than a single feature texture algorithm and also than a combination of Tamura texture features and wavelet transform features. Additionally, the technique that employs an SVM classifier achieves a higher level of accuracy, up to 99%.
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Khan, A., Rajvee, M.H., Deekshatulu, B.L., Pratap Reddy, L. (2023). A Fused LBP Texture Descriptor-Based Image Retrieval System. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds) Advances in Signal Processing, Embedded Systems and IoT . Lecture Notes in Electrical Engineering, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_13
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DOI: https://doi.org/10.1007/978-981-19-8865-3_13
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