Learning the semantics of images by using unlabeled samples

In this paper, we have proposed a novel framework to achieve more effective classifier training by using unlabeled samples. By integrating concept hierarchy for semantic image concept organization, a hierarchical mixture model is proposed to enable multi-level image concept modeling and hierarchical classifier training. To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy, we have proposed a novel adaptive EM algorithm to achieve more effective classifier training with higher prediction accuracy. To effectively learn the classifiers for the higher-level semantic image concepts, we have also proposed a novel technique for classifier combining by using hierarchical mixture model. The experimental results on two large-scale image databases are also provided.

[1]  W. Eric L. Grimson,et al.  Configuration based scene classification and image indexing , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[4]  Changshui Zhang,et al.  Competitive EM algorithm for finite mixture models , 2004, Pattern Recognit..

[5]  David A. Forsyth,et al.  Matching Words and Pictures , 2003, J. Mach. Learn. Res..

[6]  Robert A. Jacobs,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

[7]  Daphne Koller,et al.  Hierarchically Classifying Documents Using Very Few Words , 1997, ICML.

[8]  David A. Forsyth,et al.  Learning the semantics of words and pictures , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  Fabio Gagliardi Cozman,et al.  Unlabeled Data Can Degrade Classification Performance of Generative Classifiers , 2002, FLAIRS.

[10]  Tom M. Mitchell,et al.  Improving Text Classification by Shrinkage in a Hierarchy of Classes , 1998, ICML.

[11]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Paul A. Viola,et al.  A Non-Parametric Multi-Scale Statistical Model for Natural Images , 1997, NIPS.

[13]  Martial Hebert,et al.  Man-made structure detection in natural images using a causal multiscale random field , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[14]  Qi Tian,et al.  Discriminant-EM algorithm with application to image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[15]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[17]  Tommi S. Jaakkola,et al.  Information Regularization with Partially Labeled Data , 2002, NIPS.

[18]  Nuno Vasconcelos,et al.  Learning Mixture Hierarchies , 1998, NIPS.