Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning semantic object parts for object categorization
Image and Vision Computing
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Efficient Object Pixel-Level Categorization Using Bag of Features
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
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Recent works in object recognition often use visual words, i.e. vector quantized local descriptors extracted from the images. In this paper we present a novel method to build such a codebook with class representative vectors. This method, coined Cluster Precision Maximization (CPM), is based on a new measure of the cluster precision and on an optimization procedure that leads any clustering algorithm towards class representative visual words. We compare our procedure with other measures of cluster precision and present the integration of a Reciprocal Nearest Neighbor (RNN) clustering algorithm in the CPM method. In the experiments, on a subset of the the Caltech101 database, we analyze several vocabularies obtained with different local descriptors and different clustering algorithms, and we show that the vocabularies obtained with the CPM process perform best in a category-level object recognition system using a Support Vector Machine (SVM).