Exemplar-based Object Layout

Summary

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. The combined problem, usually referred to as object layout, is approached with models that require intense training. In (Lim et al., 2011) this issue is addressed 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. It turns out that it is possible to effectively represent an object class with enough image exemplars, in combination with image retrieval techniques, and statistical modeling, to obtain state-of-the-art object recognition performance with minimal training efforts.

References

1. ISVC

   Multi-class Object Layout with Unsupervised Image Classification and Object Localization Lim, S., Doretto, G., and Rittscher, J. In International Symposium on Visual Computing, 2011. Oral abstract bibTeX pdf

   Recognizingthepresenceofobjectclassesinanimage,orimageclas- sification, has become an increasingly important topic of interest. Equally impor- tant, 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 ap- proach by itself is brittle towards intraclass and viewpoint variations, we achieve robustness by introducing a novel Conditional Random Field model that facili- tates a straightforward accept/reject decision of the localized object classes. Per- formance of our approach on the PASCAL Visual Object Challenge 2007 dataset demonstrates its efficacy.

   @inproceedings{limDR11isvc,
     abbr = {ISVC},
     author = {Lim, S. and Doretto, G. and Rittscher, J.},
     title = {Multi-class Object Layout with Unsupervised Image Classification
     	and Object Localization},
     booktitle = {International Symposium on Visual Computing},
     year = {2011},
     pages = {577--589},
     bib2html_pubtype = {Conferences},
     bib2html_rescat = {Object Detection, Context, Object Classification},
     file = {limDR11isvc.pdf:doretto/conference/limDR11isvc.pdf:PDF},
     owner = {doretto},
     timestamp = {2010.12.20},
     wwwnote = {Oral}
   }