Quantitative and qualitative performance evaluations of artificially-produced images

Even though researches in content-based image retrieval are one of the most intensive research areas of information retrieval, they almost certainly focused on retrieving natural images or photographs. Artificially-produced images are stylized form of natural, real-life objects. Research for these images have received less attention, thus this paper attempts to intensify work in this area by implementing quantitative and qualitative performance evaluation of artificially-produced images. Fifty selected images are used as sample queries and they are retrieved based on fusion of geometric shape descriptors. Similarities of these images are measured using Euclidean distance and performances of the retrievals are evaluated using both quantitative and qualitative tests. Quantitative test using recall-precision rate and hit-miss ratio showed that geometric shape descriptors are effective in retrieving the images. However, qualitative test results demonstrate agreement between the system and the interviewed people in the assignment of similarity ranks is at average level.