Comprehensive Analysis of Color and Texture Descriptors for Content-Based Image Retrieval (CBIR) Systems

I thoroughly enjoyed and appreciated your comprehensive article on color and texture descriptors for Content-Based Image Retrieval (CBIR) systems. The technical insights and methodological explanations you presented were particularly valuable for understanding feature extraction processes. To further enhance the article's impact, I would suggest incorporating more practical implementation examples demonstrating how color histograms (using RGB/HSV color spaces) and texture features (such as Gabor filters or Local Binary Patterns) can be integrated into CBIR pipelines. Specifically, you could provide code snippets illustrating how these descriptors are implemented in different industry applications - for instance, using color coherence vectors for fashion recommendation systems, Haralick texture features for architectural material classification, or wavelet-based texture analysis for medical image diagnostics. Additionally, exploring the computational complexity of different feature extraction algorithms and their scalability limitations would be beneficial. You might discuss optimization techniques like dimensionality reduction using PCA or feature selection methods to address performance constraints. Including comparative analysis of different descriptor combinations and their impact on retrieval accuracy metrics (precision/recall) would provide readers with a more comprehensive understanding of practical implementation challenges and future research directions in CBIR systems.