Image dimensionality reduction based on the HSV feature

How to reduce more of the image dimensions without losing the main features of the image is highlighted in the research of Web content-based image retrieval. This paper started by analysis of commonly used methods for the dimension reduction of Web images, followed by proposing dimensionality reduction method that is based on HSV features, where the HSV color histogram intersection was used as the function of similarity judgments. And the concept of intrinsic dimension was referenced to reduce the amount of calculation on the image dimensionality reduction. Finally, some improvements were made on the traditional genetic algorithm by use of the image similarity function as the self-adaptive judgment function to improve the genetic operators, thus achieving a Web image dimensionality reduction and similarity retrieval. The results showed that this method has greatly improved the image retrieval in time and precision rates.