A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Efficiency of a Good But Not Linear Set Union Algorithm
Journal of the ACM (JACM)
Saliency, Scale and Image Description
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Filters and Their Robustness
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Finding stable salient contours
Image and Vision Computing
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Extracting regions that are noticeably different from their surroundings, so called salient regions, is a topic of considerable interest for image retrieval. There are many current techniques but it has been shown that SIFT and MSER regions are among the best. The SIFT methods have their basis in linear scale-space but less well known is that MSERs are based on a non-linear scale-space. We demonstrate the connection between MSERs and morphological scale-space. Using this connection, MSERs can be enhanced to form a saliency tree which we evaluate via its effectiveness at a standard image retrieval task. The tree out-performs scale-saliency methods. We also examine the robustness of the tree using another standard task in which patches are compared across images transformations such as illuminant change, perspective transformation and so on. The saliency tree is one of the best performing methods.