Capturing image semantics with low-level descriptors

: We conducted psychophysical experiments to gain insight into the semantic categories that guide the human perception of image similarity. We analyzed the perceptual data using multidimensional scaling (MDS) and hierarchical clustering (HC). Based on this analysis we established the most important semantic categories in the perception of image similarity. We then used these data to discover low-level features that best describe each category. Finally, we devised an image similarity metric that embodies our findings and models the behavior of subjects in categorizing images and measuring their similarity. Our results can be used for enhancement of current image/video retrieval methods, better organization of large image databases, development of more intuitive navigation schemes, browsing methods and user interfaces.

• Type: Conference
• Year: 2001
• Where: ICIP
• Authors: Aleksandra Mojsilovic, Bernice E. Rogowitz
• Keywords: ICIP 2001, Image Processing, Image Similarity, Image/video Retrieval Methods, Important Semantic Categories