TY - JOUR
T1 - Multiple kernel-based multi-instance learning algorithm for image classification
AU - Li, Daxiang
AU - Wang, Jing
AU - Zhao, Xiaoqiang
AU - Liu, Ying
AU - Wang, Dianwei
PY - 2014/7/1
Y1 - 2014/7/1
N2 - In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a "word-space" from instances based on a collection of "visual-words" generated by affinity propagation (AP) clustering method. After calculating the distance between a "visual- word" and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the "word-space". In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications.
AB - In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a "word-space" from instances based on a collection of "visual-words" generated by affinity propagation (AP) clustering method. After calculating the distance between a "visual- word" and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the "word-space". In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications.
KW - Affinity propagation (AP)
KW - Cluster analysis
KW - Image classification
KW - Image retrieval
KW - Multi-instance learning (MIL)
KW - Multiple kernel learning (MKL)
KW - Support vector machines
KW - Visual words
UR - http://www.scopus.com/inward/record.url?scp=84899476100&partnerID=8YFLogxK
U2 - 10.1016/j.jvcir.2014.03.011
DO - 10.1016/j.jvcir.2014.03.011
M3 - Article
AN - SCOPUS:84899476100
VL - 25
SP - 1112
EP - 1117
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
SN - 1047-3203
IS - 5
ER -