资 源 简 介
This paper introduces a new method of codebookbased
image categorization by building the codebook using
scored and selected local features in the image. Different from
traditional clustering-based codebook generation that may
lead to codeword uncertainty and plausibility, the proposed
Matching and Consensus (M&C) process follows the
paradigm of feature selection: Based on distance metrics, the
M&C process examines salient local features recurring over
training images and produces scores that quantify the levels of
relevance of the features to the image categories. By selecting
features with the highest scores into the codebook, the method
is expected to filter out non-representative candidates and
keeps the most informative codewords for the category. We
evaluate on five image sets for tasks of binary object
identification and multi-class biological image classification.
Experiments show that our method promotes very
parsimonious codebooks that contain highly representative
