Object categorization via local kernels

Barbara Caputo, Christian Wallraven, Maria Elena Nilsback

Research output: Contribution to journalConference articlepeer-review

19 Citations (Scopus)


This paper considers the problem of multi-object categorization. We present an algorithm that combines support vector machines with local features via a new class of Mercer kernels. This class of kernels allows us to perform scalar products on feature vectors consisting of local descriptors, computed around interest points (like corners); these feature vectors are generally of different lengths for different images. The resulting framework is able to recognize multi-object categories in different settings, from lab-controlled to real-world scenes. We present several experiments, on different databases, and we benchmark our results with state-of-the-art algorithms for categorization, achieving excellent results.

Original languageEnglish
Pages (from-to)132-135
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Publication statusPublished - 2004
Externally publishedYes
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 2004 Aug 232004 Aug 26

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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