Title
Confidence Rated Boosting Algorithm For Generic Object Detection
Abstract
In this paper we propose a confidence rated boosting algorithm based on Ada-boost for generic object detection. Confidence rated Ada-boost algorithm has not been applied to generic object detection problem; in that sense our work is novel. We represent images as bag of words, where the words are SIFT descriptors extracted over some interest points. We compare our boosting algorithm to another version of boosting algorithm called Gentle-boost. Our approach generalizes well and performs equal or better than Gentle-boost. We show our results on four categories from the Caltech data sets, in terms of ROC curves.
Year
DOI
Venue
2008
10.1109/ICPR.2008.4761184
19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6
Keywords
Field
DocType
roc curve,learning artificial intelligence,classification algorithms,feature extraction,visualization,bag of words,boosting
Bag-of-words model,Object detection,Scale-invariant feature transform,Data set,Pattern recognition,Visualization,Computer science,Feature extraction,Artificial intelligence,Boosting (machine learning),Statistical classification,Machine learning
Conference
ISSN
Citations 
PageRank 
1051-4651
1
0.37
References 
Authors
11
2
Name
Order
Citations
PageRank
Nayyar Abbas Zaidi1919.88
David Suter22247126.18