Improving the performance of content-based image retrieval systems

Our world is dominated by visual information and a tremendous amount of such information is being added day-by-day. It would be impossible to cope with this explosion of visual data, unless the images are organized such that we can retrieve them efficiently and effectively. At the core of content-based image retrieval (CBIR) is the requirement that database elements must be indexed to facilitate retrieval in an efficient manner. Most existing image retrieval systems are text-based, but images frequently have little or no accompanying textual information. Problems with text-based access to images have prompted increasing interest in the development of image-based solutions. On the other hand, CBIR relies on the characterization of primitive features such as color, shape, and texture that can be automatically extracted from images themselves. Hence, the field of CBIR focuses on intuitive and efficient methods for retrieving images from a database based solely on the content contained in the images. This study introduces a novel clustering methodology based on the gradient of images using the concept of “spin-up” and “spin-down” states derived from statistical mechanics to improve the speed of retrieval and improve the accuracy of retrieval in comparison to the traditional color histogram L1-norm retrieval methodology. By expanding the interpretation of color in images to include a gradient-based description, a new indexing method for content-based retrieval of images from an image database is developed for the reduction of false positives in the retrieval process.