A comparison study on appearance-based object recognition

Appearance-based methods are mostly exploited in the recognition of specific objects, especially faces; while methods with local features are often applied to the recognition of generic objects. Only few works report the performance of appearance-based methods applied to generic object recognition. This paper offers a comparison study to extend our understanding in this regard. The appearance features considered include those extracted by PCA, DCT, and Gabor Transformation, and the classifiers include kNN, LDA, Naive Bayes, artificial neutral networks and support vector machines. We assume that the objects in the training data can be segmented manually, but those in the test data must be segmented automatically. Therefore, a view-based segmentation approach is proposed to meet this requirement. Experiments were conducted on the COIL-100 database to specify which pair of appearance feature and classifier yields the best performance.