Novel Techniques for Color and Texture Feature Extraction?

Content based image retrieval (CBIR) is a challenging problem due to large size of the image database, difficulty in recognizing images, difficulty in devising a query and evaluating results in terms of semantic gap, computational load to manage large data files and overall retrieval time. Feature extraction is initial and important step in the design of content based image retrieval system. Feature extraction is a means of extracting unique and valuable information from the image. These features are also termed as signature of image. Feature extraction of the image in the database is done offline therefore it does not contribute significantly in computational complexity. Humans tend to differentiate images based on color, therefore color features are mostly used in CBIR. Color moment is mostly used to represent color features especially when image contain just an object. Regularity, directionality, smoothness and coarseness are some of the texture properties perceived by human eye. Gabor filter and wavelet transform for texture feature extraction has proved to be very effective in describing visual content via multi-resolution analysis. The paper mainly gives the brief ideas of existing retrieval techniques. Also paper gives the comparative analysis of mentioned techniques with different metrics.