Automatic System for Extraction of Content-Based Characteristics from Digital Images

In this paper we expose the development of a CBIR system (Content-based Image Retrieval) that is able to retrieve images from a corpus based upon the image content. In order to obtain such functionality, the system establishes a set of characteristics which will be automatically generated. This allows the system to univocally identify each image from the collection. The sort of characteristics is diverse and they are related to concepts such as entropy, Gabor lters and image size. After the calculation of characteristics of each image, a calibration process is performed whereby the system estimates the best weight for each characteristic. This estimation makes use of a calibration algorithm and a set of experiments, and the result is the in uence of each characteristic in the main function that is used for the retrieval process. The calibration process starts in an equally balanced situation (all the characteristics have the same in uence in the main function), and after several iterations the weight for each characteristic is xed. The following task is the image validation, where the modi cations to the main function are veri ed so as to ensure that the new function is better than the previous one. Finally, the image retrieval process is performed according to the ImageCLEFmed rules. The retrieval results have not been the expected ones, but we must say they are a good starting point that makes us establish several work lines for the future.