Paper Info
Title
Multi-class object layout with unsupervised image classification and object localization
Abstract
Recognizing the presence of object classes in an image, or image classification, has become an increasingly important topic of interest. Equally important, however, is also the capability to locate these object classes in the image. We consider in this paper an approach to these two related problems with the primary goal of minimizing the training requirements so as to allow for ease of adding new object classes, as opposed to approaches that favor training a suite of object-specific classifiers. To this end, we provide the analysis of an exemplar-based approach that leverages unsupervised clustering for classification purpose, and sliding window matching for localization. While such exemplar based approach by itself is brittle towards intraclass and viewpoint variations, we achieve robustness by introducing a novel Conditional Random Field model that facilitates a straightforward accept/reject decision of the localized object classes. Performance of our approach on the PASCAL Visual Object Challenge 2007 dataset demonstrates its efficacy.
Year
DOI
Venue
2011
10.1007/978-3-642-24028-7_53
ISVC (1)
Keywords
Field
DocType
exemplar-based approach,training requirement,object localization,pascal visual object challenge,unsupervised image classification,important topic,classification purpose,new object class,localized object class,object class,novel conditional random field,image classification
Computer science,Robustness (computer science),Artificial intelligence,Contextual image classification,Cluster analysis,Standard test image,Conditional random field,Computer vision,Sliding window protocol,Method,Pattern recognition,Point cloud,Machine learning
Conference
Citations 
PageRank 
References 
1
0.52
28
Authors
3
Name
Order
Citations
PageRank
Ser-Nam Lim112719.49
Gianfranco Doretto2102678.58
Jens Rittscher368667.07