23 December 1999 Combining fast search and learning for fast similarity search
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Abstract
In this paper,we propose a new scalable simultaneous learning and indexing technique for efficient content-based retrieval of images that can be described by high- dimensional feature vectors. This scheme combines the elements of an efficient nearest neighbor search algorithm, and a relevance feedback learning algorithm which refines the raw feature space to the specific subjective needs of each new application, around a commonly shared compact indexing structure based on recursive clustering. Consequently, much better time efficiency and scalability can be achieved as compared to those techniques that do not make provisions for efficient indexing or fast learning steps. After an overview of the current related literature, and a presentation of our objectives and foundations, we describe in detail the three aspects of our technique: learning, indexing and similarity search. We conclude with an analysis of the objectives met, and an outline of the current work and considered future enhancements and variations on this technique.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hooman Vassef, Chung-Sheng Li, Vittorio Castelli, "Combining fast search and learning for fast similarity search", Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); doi: 10.1117/12.373570; https://doi.org/10.1117/12.373570
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