Content based image retrieval system based on semantic information using color, texture and shape features

Due the rapid growth in the area of Digital Image Processing the semantic based techniques are also been emerged for an efficient processing. In order to achieve an efficient result; this paper a proposed a technique for the generation of image content descriptor with three features viz., Color auto-Correlogram, Gabor Wavelet and Wavelet Transform. Color Auto-Correlogram Feature is associated with color information of an image which is derived from the RGB color space of an image. The Gabor Wavelet Feature is has the texture information to extract the textural features associated with the image and the Wavelet Transform Feature is linked with shape information in the extraction of edges in an image. The feature extraction process is accomplished based on the input query image from the IDB and the features are stored in a feature dataset. The Manhattan distance is applied on the user given query image and feature vector computed from database images for measuring similarity. Finally, the proposed technique retrieves the meaningful image from the image database which satisfies the user expectation. The performance of the retrieval system has been analyzed by the performance measures Precision and Recall. The efficiency of the proposed feature descriptor is tested for CBIR system using Corel image database, Li image database and Caltech-101 image database.