Abstract
We are interested in incrementally discovering the set of object classes present in a scalable database of images. This paper describes a graph-based framework for learning the set of object classes in a weakly supervisedly manner. Rather than making use of the ”Bag-of-Features (BoF)” approach widely used in current work on object recognition, we represent each image by a graph using a group of selected local invariant features. Using local feature matching and iterative Procrustes alignment, we perform graph matching and compute a similarity measure. Borrowing the idea of query expansion, we develop a similarity propagation based graph clustering (SPGC) method. Using this method class specific clusters of the graphs can be obtained. Such a cluster can be generally represented by using a higher level graph model whose vertices are the clustered graphs, and the edge weights are determined by the pairwise similarity measure. Experiments are performed on a dataset, in which the number of images increases from 1 to 50K and the number of objects increases from 1 to over 500. Some objects have been discovered with total recall and a precision 1 in a single cluster.
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Xia, S., Hancock, E.R. (2010). Incrementally Discovering Object Classes Using Similarity Propagation and Graph Clustering. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_36
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DOI: https://doi.org/10.1007/978-3-642-12297-2_36
Publisher Name: Springer, Berlin, Heidelberg
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