An object-based image retrieval system using an inhomogeneous diffusion model
Andrea Kutics, M. Nakajima, Toshiharu Ieki, Naoki Mukawa · 1999
This paper proposes a novel object-based method to similar image retrieval by representing an image by its salient object regions as well as associated color texture and shape features. The major obstacle in developing such methods is the difficulty of accurately segmenting the image into prominent regions. To overcome this difficulty we applied an inhomogeneous diffusion model to both color and texture features to quasi-accurately detect salient regions in the image. Characteristic color texture and shape features of these regions are obtained on the basis of diffusion results. These features are invariant to rotation. Scale invariance was also achieved for the color and shape features. A suitable user interface was developed to facilitate object-based searches. Enabling the user to specify the objects he/she is looking for ensures a higher level of performance and flexibility. Experiments conducted on a large number of images taken from Corel and Kodak photo-CD data show that the method performs well for a large variety of natural images.