Image Retrieval Based on Color and Texture Features Using Color Moments and Gabor Wavelet Transform

In this article, we provide a comprehensive explanation of image retrieval techniques based on color and texture features, focusing on the implementation of color moments and Gabor wavelet transforms. These methodologies enable more accurate and efficient image retrieval by capturing richer image characteristics. Color moments offer an effective approach for describing color distribution in images through statistical measures (mean, standard deviation, skewness), typically computed by dividing images into regions and calculating moment values for each color channel. Gabor wavelet transform excels at extracting texture information by applying multi-scale and multi-orientation filters that simulate human visual system responses, implemented through convolution operations with Gabor filter banks at different frequencies and angles. The integration of these techniques provides a robust framework for image data processing, significantly improving retrieval precision and operational efficiency. We will examine the underlying principles and practical applications of these methods, supplemented with real-world case studies demonstrating their performance advantages. Implementation considerations include feature vector normalization techniques for color moments (often using HSV color space conversion) and Gabor filter parameter optimization (wavelengths ranging 2-10 pixels, orientations 0-180 degrees in 30° increments). The retrieval process typically involves computing similarity distances (Euclidean or Cosine distance) between feature vectors extracted from query and database images.