Multi-objects recognition for distributed intelligent sensor networks

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

[1]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[2]  Stephen Grossberg,et al.  Neural Substrates of Adaptively Timed Reinforcement, Recognition, and Motor Learning , 1995 .

[3]  Stephen Grossberg,et al.  Adaptive Resonance Theory , 2010, Encyclopedia of Machine Learning.

[4]  C. A. Murthy,et al.  A connectionist model for category perception: theory and implementation , 1993, IEEE Trans. Neural Networks.

[5]  Sergios Theodoridis,et al.  Pattern Recognition, Third Edition , 2006 .

[6]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[7]  Paul K. Davis,et al.  Improving the Composability of Department of Defense Models and Simulations , 2004 .

[8]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[9]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[10]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[11]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[13]  Paul R. Muessig,et al.  Optimizing the selection of VV&A activities: a risk/benefit approach , 1997, WSC '97.

[14]  Andreas Tolk,et al.  M&S within the Model Driven Architecture , 2004 .

[15]  S. Akaho,et al.  The EM algorithm for multiple object recognition , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[17]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[18]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.