Content-based image retrieval system based on combined and weighted multi-features

This paper, we proposed a novel framework for combining and weighting all of three i.e. color, shape and texture features to achieve higher retrieval efficiency. The color feature is extracted by quantifying the YUV color space and the color attributes like the mean value, the standard deviation, and the image bitmap of YUV color space is represented. The texture features are obtained by the entropy based on the gray level cooccurrence matrix and the edge histogram descriptor of an image. The shape feature descriptor is derived from Fourier descriptors (FDs) and the FDs derived from different signatures. When computing the similarity between the query image and target image in the database, normalization information distance is also used for adjusting distance values into the same level. And then the linear combination has used to combine the normalized distance of the color, shape and texture features to obtain the similarity as the indexing of image. Furthermore, an experimental results indicated, a weight variation to achieve higher retrieval efficiency and the proposed technique indeed outperforms other schemes in terms of the accuracy and efficiency.