Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Towards Multi-View Object Class Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Speeded-Up Robust Features (SURF)
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On scalable distributed coding of correlated sources
IEEE Transactions on Signal Processing
Integrating multiple model views for object recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Distributed source coding using syndromes (DISCUS): design and construction
IEEE Transactions on Information Theory
A Maximum Entropy Approach to Unsupervised Mixed-Pixel Decomposition
IEEE Transactions on Image Processing
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Performing multi-view object recognition in distributed camera networks is of great importance but also of great challenge since the scarce resource within the network prohibits large amount of data transfer. In this paper, we study the problem of feature-based distributed object recognition where redundancy in SIFT features across multiple views is explored without the requirement of any known statistics of the environment. We present a novel concept to interpret the SIFT features from spectral unmixing point of view where SIFT features from local views of an object are modeled as linear mixtures of a small set of signature vectors, referred to as the endmembers, with associated weight vectors satisfying two conditions, sum to one and nonnegative. We show, through empirical study, that this set of endmembers is unique and sufficient to recognize individual object and yet, the number of endmembers is much smaller than the number of SIFT feature points detected, thus dramatically saving network bandwidth. We perform this feature unmixing process in a two-layer scheme to realize distributed object recognition, where unmixing is first applied at individual camera nodes to extract "local endmembers" based on local views. Only these local endmembers need to be transferred to the base station for further processing. At the base station, the ensemble of the local endmembers is undergone another unmixing process to extract the so-called "global endmembers" for object recognition purpose. Experimental results show that the feature unmixing-based distributed object recognition can achieve same level of recognition accuracy compared to the usage of the original set of SIFT features, but with much reduced data transmission.