International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
Development of visual shape primitives (learning, object recognition)
Development of visual shape primitives (learning, object recognition)
Journal of Cognitive Neuroscience
Blind source separation with pattern expression NMF
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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This article introduces a segmentation method to automatically extract object parts from a reduced set of images. Given a database of objects and dividing all of them using local color histograms, we obtain an object part as the conjunction of the most similar ones. The similarity measure is obtained analyzing the behaviour of a local vector with respect to the whole object database. Furthermore, the proposed technique is able to associate an energy to each object part being possible to find the most discriminant object parts. We present the non-negative matrix factorization (NMF) technique to improve the internal data representation by compacting the original local histograms (50D instead of 512D). Moreover, the NMF based projected histograms only contain a few activated components and this fact improves the clustering results with respect to the use of the original local color histograms. We present a set of experimental results validating the use of the NMF in conjunction with the clustering technique.