Similarity templates for detection and recognition
- 25 August 2005
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (10636919)
- https://doi.org/10.1109/cvpr.2001.990479
Abstract
This paper investigates applications of a new representation for images, the similarity template. A similarity template is a probabilistic representation of the similarity of pixels in an image patch. It has application to detection of a class of objects, because it is reasonably invariant to the color of a particular object. Further, it enables the decomposition of a class of objects into component parts over which robust statistics of color can be approximated. These regions can be used to create a factored color model that is useful for recognition. Detection results are shown on a system that learns to detect a class of objects (pedestrians) in static scenes based on examples of the object provided automatically by a tracking system. Applications of the factored color model to image indexing and anomaly detection are pursued on a database of images of pedestrians.Keywords
This publication has 9 references indexed in Scilit:
- Automatic hierarchical classification using time-based co-occurrencesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Pedestrian detection using wavelet templatesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Learning from one example through shared densities on transformsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Fast approximate energy minimization via graph cutsIeee Transactions On Pattern Analysis and Machine Intelligence, 2001
- Learning patterns of activity using real-time trackingIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Learning the parts of objects by non-negative matrix factorizationNature, 1999
- Training templates for scene classification using a few examplesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Distributional clustering of English wordsPublished by Association for Computational Linguistics (ACL) ,1993
- Comparing images using the Hausdorff distanceIeee Transactions On Pattern Analysis and Machine Intelligence, 1993