Research Paper on ICCV, GLCM, AND DWT-MSLBP DESCRIPTORS FOR ROBUST CONTENT-BASED IMAGE RETRIEVAL

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Title: ICCV, GLCM, AND DWT-MSLBP DESCRIPTORS FOR ROBUST CONTENT-BASED IMAGE RETRIEVAL

Abstract: Based on a given query, content-based image retrieval (CBIR) extracts visually comparable images from a dataset. A CBIR system ranks the dataset images based on how similar a query and the image contents in a dataset are. The retrieval of related images using a novel framework based on color and texture features is presented in this work. In addition to DWT-MSLBP, we computed texture features using a gray-level co-occurrence matrix (GLCM) and color features using an improved color coherence vector (ICCV) (which is derived from applying a modified multi-scale local binary pattern [MS-LBP] over a discrete wavelet transform [DWT], resulting in powerful textural features). Principal component analysis (PCA) and linear discriminant analysis are used to compute the best features (LDA). The proposed study chooses the number of principal components/eigenvectors in PCA using a variance-based methodology. LDA chooses robust features from the set of features while PCA preserves healthy ones with a variance of 99.99%. Four benchmark datasets with Euclidean and city-block distances were used to test the proposed method. All of the identified cutting-edge literature methods pale in comparison to the suggested method.

Keywords: content-based image retrieval, improved color coherence vector, gray-level co-occurrence matrix, discrete wavelet transform, multi-scale local binary pattern, principal component analysis, linear discriminant analysis

Paper Quality: SCOPUS / Web of Science Level Research Paper

Paper type: Analysis Based Research Paper

Subject: Computer Science

Writer Experience: 20+ Years

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