中国科学院自动化研究所机构知识库

The combination of local features with sparse technique has improved
image classification performance dramatically in recent years.
Although very effective, this strategy still has two shortcomings.
First, local features are often extracted in a pre-defined way (e.g.
SIFT with dense sampling) without considering the classification
task. Second, the codebook is generated by sparse coding or its
variants by minimizing the reconstruction error which has no direct
relationships with the classification process. To alleviate the two
problems, we propose a novel boosted local features method with
random orientation and location selection. We first extract local
features with random orientation and location using a weighting
strategy. This randomization process makes us to extract more types
of information for image representation than pre-defined methods.
These extracted local features are then encoded by sparse
representation. Instead of generating the codebook in a single
process, we construct a series of codebooks and the corresponding
encoding parameters of local features using a boosting strategy. The
weights of local features are determined by the classification
performances of learned classifiers. In this way, we are able to
combine the local feature extraction and encoding with classifier
training into a unified framework and gradually improve the image
classification performance. Experiments on several public image
datasets prove the effectiveness and efficiency of the proposed
method.