An oil painters recognition method based on cluster multiple kernel learning algorithm

A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly

VITALAS at TRECVID-2008

In this paper, we present our experiments in TRECVID 2008 about High-Level feature extraction task. This is the first year for our participation in TRECVID, our system adopts some popular approaches that other workgroups proposed before. We proposed 2 advanced low-level features NEW Gabor texture descriptor and the Compact-SIFT Codeword histogram. Our system applied well-known LIBSVM to train the SVM classifier for the basic classifier. In fusion step, some methods were employed such as the Voting, SVM-base, HCRF and Bootstrap Average AdaBoost(BAAB)