Conference proceeding
Large-scale Image Classification Using Supervised Spatial Encoder
2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), pp.581-584
International Conference on Pattern Recognition
01 Jan 2012
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Spatial pyramid matching (SPM) component is part of most state-of-art image classification methods. SPM encodes spatial distribution of image features, in an unsupervised fashion, by partitioning an image into regions at multiple scales and concatenating feature vectors for these regions. In this paper we propose to replace the unsupervised SPM procedure with a supervised two-stage feature selection that requires the image partitioned at a single scale. Experimental results show the proposed method performs statistically significantly better than the SPM baseline.
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Details
- Title
- Large-scale Image Classification Using Supervised Spatial Encoder
- Creators
- Dmitriy Bespalov - Drexel Univ, Philadelphia, PA 19104 USAYanjun Qi - NEC Labs Amer, Princeton, NJ USABing Bai - NEC Labs Amer, Princeton, NJ USAAli Shokoufandeh - Drexel UniversityIEEE
- Publication Details
- 2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), pp.581-584
- Series
- International Conference on Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Identifiers
- 991019170380204721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence