Automatic tag saliency ranking for stereo images
With the rapid advances in 3D capture and display technology, tag ranking for stereo images will be a potential application on web image retrieval. Directly extending the existing approaches to stereo images is problematic as it may neglect the representative 3D elements. In this paper, a novel automatic tag saliency ranking algorithm for stereo images is presented. Specifically, a novel method of interacting with stereo images is proposed to segment the two images into regions simultaneously. Then tags annotated on the image-level are propagated to the region-level via an improved multi-instance learning algorithm. In the next, a new 3D saliency detection algorithm is proposed using occlusion cues along with the contrast in color and disparity. And finally, tags are re-ranked according to the saliency values of the corresponding regions. Moreover, to evaluate the performance of our approach, an annotated stereo image dataset is set up. The experimental results on this dataset demonstrate the effectiveness of our approach.
stereo image tag ranking multi-instance learning 3d saliency detection