Wavelet-based Image Retrieval Using Color Spatial Information

This paper provides a new image retrieval approach—Wavelet-based image retrieval using color spatial information. First the images are wavelet-transformed. Partition is used to extract the spatial information of the low and high frequency parts and by coding,image can be transformed into a text-image. So images can be analyzed by mature text model.Color is a common used feature in content-based image retrieval for it simplifies object identification. The traditional approach to using the color information of an image is color histogram, which is insensitive to translation and rotation. But its drawback is prone to yield false hits in large database because of lacking spatial information. To solute the problem, we try to wavelet-transform the image and combine spatial information with color feature to improve the performance of content-based image retrieval. This is achieved by partitioning images in the training set into fixed size cells and, for each cell, extracting a local color histogram as the color invariant feature of the cell. All of the color invariant features are clustered into a number of patterns. Thus all the images in the database can be regarded as a collection of those patterns. Images are recognized by their patterns, which take a step to retrieve image by symbolic notions. Thereby the well-developed text retrieval method can be applied for image query and index through such symbolic descriptions. Experimental results show that the new method is robust in retrieving images.This paper wavelet-transform the images and uses information entropy as a measure of optimal scale and experimental results show it is reasonable.