Image Retrieval Using Color and Texture Features

In color- and texture-based image retrieval systems, we enhance image features through multiple color spaces and texture descriptors. This approach significantly improves retrieval accuracy and performance efficiency. For color feature extraction, implementations typically utilize color histograms in RGB/HSV spaces with quantization techniques, while texture analysis employs algorithms like Gabor filters, Local Binary Patterns (LBP), or Gray-Level Co-occurrence Matrix (GLCM) to capture spatial patterns. Image preprocessing involves applying various filters and transformation algorithms to extract richer feature information. Common implementations include Gaussian blur for noise reduction, edge detection using Sobel or Canny operators, and contrast enhancement through histogram equalization. Furthermore, machine learning and deep learning techniques are employed to train models that better understand and extract color-texture information. Practical implementations might include: - Feature fusion methods combining color moments with texture features - CNN architectures (like VGG or ResNet) fine-tuned for texture classification - Similarity measurement algorithms (Euclidean distance, cosine similarity) for retrieval ranking - Dimensionality reduction techniques (PCA, t-SNE) for efficient feature matching By integrating these methodologies and technologies, we can substantially enhance the capabilities and effectiveness of image retrieval systems.