A new content-based image retrieval technique using color and texture information

Feature extraction and representation is one of the most important issues in the content-based image retrieval. In this paper, we propose a new content-based image retrieval technique using color and texture information, which achieves higher retrieval efficiency. Firstly, the image is transformed from RGB space to opponent chromaticity space, and the characteristics of the color contents of an image is captured by using Zernike chromaticity distribution moments from the chromaticity space. Secondly, the texture features are extracted using a rotation-invariant and scale-invariant image descriptor in Contourlet domain, which offers an efficient and flexible approximation of early processing in the human visual system. Finally, the combination of the color and texture information provides a robust feature set for color image retrieval. Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images.

The binary image (2D signal) R and its reconstructed image. The directional subbands and lowpass subband of 3 levels Contourlet decomposition for Zoneplate image.Figure optionsDownload as PowerPoint slideHighlights
► To capture the color characteristics using Zernike chromaticity distribution moments.
► To extract the texture features using invariant image descriptor in Contourlet domain.
► To combine the color and texture information for color image retrieval.