Efficient Mining of Frequent and Distinctive Feature Configurations
- 1 January 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We present a novel approach to automatically find spatial configurations of local features occurring frequently on instances of a given object class, and rarely on the background. The approach is based on computationally efficient data mining techniques and can find frequent configurations among tens of thousands of candidates within seconds. Based on the mined configurations we develop a method to select features which have high probability of lying on previously unseen instances of the object class. The technique is meant as an intermediate processing layer to filter the large amount of clutter features returned by low- level feature extraction, and hence to facilitate the tasks of higher-level processing stages such as object detection.Keywords
This publication has 10 references indexed in Scilit:
- Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categoriesComputer Vision and Image Understanding, 2007
- A Sparse Object Category Model for Efficient Learning and Exhaustive RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Pedestrian Detection in Crowded ScenesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Learning to detect objects in images via a sparse, part-based representationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Semi-Local Affine Parts for Object RecognitionPublished by British Machine Vision Association and Society for Pattern Recognition ,2004
- Object class recognition by unsupervised scale-invariant learningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Web mining: information and pattern discovery on the World Wide WebPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Efficient mining of association rules in text databasesPublished by Association for Computing Machinery (ACM) ,1999
- Mining association rules between sets of items in large databasesPublished by Association for Computing Machinery (ACM) ,1993