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