DocumentCode :
3408865
Title :
Hierarchical object groups for scene classification
Author :
Sadovnik, Amir ; Tsuhan Chen
Author_Institution :
Dept. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
fYear :
2012
fDate :
Sept. 30 2012-Oct. 3 2012
Firstpage :
1881
Lastpage :
1884
Abstract :
The hierarchical structures that exist in natural scenes have been utilized for many tasks in computer vision. The basic idea is that instead of using strictly low level features it is possible to combine them into higher level hierarchical structures. These higher level structures provide a more specific feature and can thus lead to better results in classification or detection. Although most previous work has focused on hierarchical combinations of low level features, hierarchical structures exist on higher levels as well. In this work we attempt to automatically discover these higher level structures by finding meaningful object groups using the Minimum Description Length (MDL) principle. We then use these structures for scene classification and show that we can achieve a higher accuracy rate using them.
Keywords :
computer vision; image classification; MDL; computer vision; hierarchical object groups; hierarchical structures; minimum description length; scene classification; Accuracy; Detectors; Feature extraction; Object detection; Painting; Training; Vectors; Image Classification; Object Detection; Object Groups; Scene Classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
ISSN :
1522-4880
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2012.6467251
Filename :
6467251
Link To Document :
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