Object Categorization via Local Kernels

In this paper we consider 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.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Barbara Caputo, Christian Wallraven, Maria-Elena Nilsback
• Keywords: Computer Vision, Feature Vectors, ICPR 2004, Multi-object Categories, Multi-object Categorization