Query an Image Database by Segmentation and Content

The Recent advances on image databases have been developed and most of them consider several methods to query image, the amount of information stored is so big that it is a must to use a combination of different techniques such as image segmentation in order to reduce the dimensionality of the search space. Taking advantage of an image pictographic expressiveness together with the soundness of image segmentation methods, it is possible to rely on an efficient method to query an image database. In this work, it is proposed a new method of image segmentation, indexation and retrieval by content. In this paper an image is not considered as a set of objects, is considered as a feature vector where its components represent a segment of color. Color is treated in another color space rather than to work on RGB space. For each image a fuzzy histogram is obtained in order to get for each image its own signature together whit its own feature vector. Fuzzy theory is applied to solve color uncertainty, which it comes from color quantification and human perception of colors. The whole set of images, which are in RGB representation are transformed to LAB model, obtaining better color representation in order to obtain a feature vector together with wavelet coefficients.