23 December 1997 S-STIR: similarity search through iterative refinement
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Abstract
Similarity retrieval of images based on texture and color features has generated a lot of interests recently. Most of these similarity retrievals are based on the computation of the Euclidean distance between the target feature vector and the feature vectors in the database. Euclidean distance, however, does not necessarily reflect either relative similarity required by the user. In this paper, a method based on nonlinear multidimensional scaling is proposed to provide a mechanism for the user to dynamically adjust the similarity measure. The results show that a significant improvement on the precision versus recall curve has been achieved.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chung-Sheng Li, John R. Smith, Vittorio Castelli, "S-STIR: similarity search through iterative refinement", Proc. SPIE 3312, Storage and Retrieval for Image and Video Databases VI, (23 December 1997); doi: 10.1117/12.298458; https://doi.org/10.1117/12.298458
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