The Multimodal Neighborhood Signature for Modeling Object Color Appearance and Applications in Object Recognition and Image Retrieval

摘要

We propose a general-purpose color-based object model called the Multimodal Neighborhood Signature (MNS) with applications in object recognition and image retrieval. Object modeling is example-based and can cope with many appearance variations due to the image formation/rendering process. The local nature of the color representation facilitates robustness to occlusion and clutter. Unlike other methods, neither segmentation nor edge detection is required and the area of homogeneously colored regions is not used. The algorithm is simple to implement and has low storage requirements. In the reported experiments, eight recognition and two other retrieval methods are reviewed and systematically compared with MNS. Results show good and fast performance under severe scale, viewpoint, occlusion, and background change using a single image for object modeling. Although spatial information was not used and its default internal parameters were used, MNS outperformed most compared methods.