This paper introduces a new framework for image classification using local visual descriptors. The pipeline first performs a nonlinear feature transformation on descriptors, then aggregates the results together to form image-level representations, and finally applies a classification model. For all the three steps we suggest novel solutions which make our approach appealing in theory, more scalable in computation, and transparent in classification. Our experiments demonstrate that the proposed classification method achieves state-of-the-art accuracy on the well-known PASCAL benchmarks. © 2010 Springer-Verlag.
CITATION STYLE
Zhou, X., Yu, K., Zhang, T., & Huang, T. S. (2010). Image classification using super-vector coding of local image descriptors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6315 LNCS, pp. 141–154). Springer Verlag. https://doi.org/10.1007/978-3-642-15555-0_11
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