Content-based image retrieval incorporating models of human perception

We develop a system for retrieving medical images with focus objects incorporating models of human perception. The approach is to guide the search for an optimum similarity function using human perception. First, the images are segmented using an automated segmentation tool. Then, 20 shape features are computed from each image to obtain a feature matrix. Principal component analysis is performed on this matrix to reduce the number of dimensions. Principal components obtained from the analysis are used to select a subset of variables that best represents the data. A human perception of similarity experiment is designed to obtain an aggregated human response matrix. Finally, an optimum weighted Manhattan distance function is designed using a genetic algorithm utilizing the Mantel test as a fitness function. The system is tested for content-based retrieval of skin lesion images. The results show significant agreement between the computer assessment and human perception of similarity. Since the features extracted are not specific to skin lesion images, the system can be used to retrieve other types of images.