Vector Quantizing Feature Space with a Regular Lattice

Most recent class-level object recognition systems work with visual words, i.e., vector quantized local descriptors. In this paper we examine the feasibility of a dataindependent approach to construct such a visual vocabulary, where the feature space is discretized using a regular lattice. Using hashing techniques, only non-empty bins are stored, and fine-grained grids become possible in spite of the high dimensionality of typical feature spaces. Based on this representation, we can explore the structure of the feature space, and obtain state-of-the-art pixelwise classification results. In the case of image classification, we introduce a class-specific feature selection step, which takes the spatial structure of SIFT-like descriptors into account. Results are reported on the Graz02 dataset.

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Tinne Tuytelaars, Cordelia Schmid
• Keywords: Class-specific Feature Selection, Computer Vision, Feature Space, ICCV 2007, Local Descriptors, State-of-the-art Pixelwise Classification, Typical Feature Spaces