The ability to recognize and distinguish between various textures in an image is made possible by feature extraction, which is a fundamental step in computer vision and image processing. Traditional methods of texture analysis fall short of capturing the perceptual characteristics that give texture its meaning and identity. Because Tamura texture attributes were developed through research into the spatial and frequency components of textures, they offer a more precise and discriminating representation of textures. Tamura features capture significant visual qualities that are crucial for comprehending and interpreting texture. Tamura descriptors enable to characterization and comparison of various textures, enabling tasks like texture classification, segmentation, and retrieval. SIFT processes Tamura descriptors to extract scale-invariant features, enhancing the texture representation's capacity for discrimination. The suggested method was evaluated on numerous benchmark datasets, and the findings revealed that it outperforms conventional texture analysis methods in terms of precision, recall, and other performance measures. The qualitative evaluation further verified the interpretability and perceptual significance of the retrieved texture elements, proving their appropriateness for texture analysis tasks. The evaluation's findings show how well the suggested technique extracts texture features and how it might boost the effectiveness of numerous computer vision and image processing applications.