Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2012.6467251
Title: Hierarchical object groups for scene classification
Authors: Sadovnik A.
Chen T. 
Keywords: Image Classification
Object Detection
Object Groups
Scene Classification
Issue Date: 2012
Citation: Sadovnik A., Chen T. (2012). Hierarchical object groups for scene classification. Proceedings - International Conference on Image Processing, ICIP : 1881-1884. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2012.6467251
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.
Source Title: Proceedings - International Conference on Image Processing, ICIP
URI: http://scholarbank.nus.edu.sg/handle/10635/146117
ISBN: 9781467325332
ISSN: 15224880
DOI: 10.1109/ICIP.2012.6467251
Appears in Collections:Staff Publications

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