A Discriminative Data-Dependent Mixture-Model Approach for Multiple Instance Learning in Image Classification

Multiple Instance Learning (MIL) has been widely used in various applications including image classification. However, existing MIL methods do not explicitly address the multi-target problem where the distributions of positive instances are likely to be multi-modal. This strongly limits the performance of multiple instance learning in many real world applications. To address this problem, this paper proposes a novel discriminative data-dependent mixture-model method for multiple instance learning (MM-MIL) approach in image classification. The new method explicitly handles the multi-target problem by introducing a data-dependent mixture model, which allows positive instances to come from different clusters in a flexible manner. Furthermore, the kernelized representation of the proposed model allows effective and efficient learning in high dimensional feature space. An extensive set of experimental results demonstrate that the proposed new MM-MIL approach substantially outperforms several state-of-art MIL algorithms on benchmark datasets.

[1]  Horst Bischof,et al.  MIForests: Multiple-Instance Learning with Randomized Trees , 2010, ECCV.

[2]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, CVPR.

[3]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[4]  Horst Bischof,et al.  On-line semi-supervised multiple-instance boosting , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[6]  Nenghai Yu,et al.  Multiple-instance ranking: Learning to rank images for image retrieval , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Luo Si,et al.  M3IC: Maximum Margin Multiple Instance Clustering , 2009, IJCAI.

[8]  Huijun Di,et al.  A Globally Optimal Approach for 3D Elastic Motion Estimation from Stereo Sequences , 2010, ECCV.

[9]  Tomás Lozano-Pérez,et al.  A Framework for Multiple-Instance Learning , 1997, NIPS.

[10]  Larry S. Davis,et al.  Multiple instance fFeature for robust part-based object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Yixin Chen,et al.  Image Categorization by Learning and Reasoning with Regions , 2004, J. Mach. Learn. Res..

[12]  Sally A. Goldman,et al.  MISSL: multiple-instance semi-supervised learning , 2006, ICML.

[13]  Luo Si,et al.  Flexible Mixture Model for Collaborative Filtering , 2003, ICML.

[14]  Fei Wang,et al.  Interactive localized content based image retrieval with multiple-instance active learning , 2010, Pattern Recognit..

[15]  Paul A. Viola,et al.  Multiple Instance Boosting for Object Detection , 2005, NIPS.

[16]  Zhouyu Fu,et al.  An instance selection approach to Multiple instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Технология.  Springer Science+Business Media , 2013 .

[18]  Jing Hua,et al.  Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Larry S. Davis,et al.  Multiple instance fFeature for robust part-based object detection , 2009, CVPR.

[20]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[21]  Qi Zhang,et al.  EM-DD: An Improved Multiple-Instance Learning Technique , 2001, NIPS.

[22]  Mark Craven,et al.  Supervised versus multiple instance learning: an empirical comparison , 2005, ICML.

[23]  Bernhard Schölkopf,et al.  A Generalized Representer Theorem , 2001, COLT/EuroCOLT.

[24]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[25]  Jianping Fan,et al.  Correlative multi-label multi-instance image annotation , 2011, 2011 International Conference on Computer Vision.

[26]  Oded Maron,et al.  Multiple-Instance Learning for Natural Scene Classification , 1998, ICML.

[27]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[28]  David Page,et al.  Multiple Instance Regression , 2001, ICML.

[29]  Tao Mei,et al.  Concurrent Multiple Instance Learning for Image Categorization , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Ji Zhu,et al.  Kernel Logistic Regression and the Import Vector Machine , 2001, NIPS.

[31]  A. L. Ratan.  Multiple-Instance Learning for Natural Scene , 1998 .

[32]  Tao Mei,et al.  Joint multi-label multi-instance learning for image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Fei Wang,et al.  Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering , 2011, IEEE Transactions on Neural Networks.

[34]  Joachim M. Buhmann,et al.  Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[35]  Yixin Chen,et al.  MILES: Multiple-Instance Learning via Embedded Instance Selection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.