Given a query image of an object of interest, our objective is to retrieve all instances of that object with high precision from a database of scalable size. As distinct from the bag-of-feature based methods, we do not regard descriptor quantizations as "visual words". Instead a group of selected SIFT features of an object together with their spatial arrangement are represented by an attributed graph. Each graph is then regarded as a "visual word". We measure the similarity between graphs using the similarity of SIFT features and the compatibility of their arrangement. Using the similarity measure we efficiently identify the set of K nearest neighbor graphs (KNNG) using a SOM based clustering tree. We then extend the concept of "query expansion" widely used in text retrieval to develop a graph clustering method based on pairwise similarity propagation (SPGC), in that the trained KNNG information is utilized for speeding up. Using SOM based clustering tree and SPGC, we develop a framework for scalable object indexing and retrieval. We illustrate these ideas on a database of over 50K images spanning more than 500 objects. We show that the precision is substantially boosted, achieving total recall in many cases. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Xia, S., & Hancock, E. R. (2009). Pairwise similarity propagation based graph clustering for scalable object indexing and retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5534 LNCS, pp. 184–194). https://doi.org/10.1007/978-3-642-02124-4_19
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