Randomized spatial partition for scene recognition
The spatial layout of images plays a critical role in natural scene analysis. Despite previous work, e.g., spatial pyramid matching, how to design optimal spatial layout for scene classification remains an open problem due to the large variations of scene categories. This paper presents a novel imag...
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Main Authors: | , , |
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Other Authors: | |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2013
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/100601 http://hdl.handle.net/10220/17891 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | The spatial layout of images plays a critical role in natural scene analysis. Despite previous work, e.g., spatial pyramid matching, how to design optimal spatial layout for scene classification remains an open problem due to the large variations of scene categories. This paper presents a novel image representation method, with the objective to characterize the image layout by various patterns, in the form of randomized spatial partition (RSP). The RSP-based
image representation makes it possible to mine the most descriptive image layout pattern for each category of scenes, and then combine them by training a discriminative classifier, i.e., the proposed ORSP classifier. Besides RSP image
representation, another powerful classifier, called the BRSP classifier, is also proposed. By weighting a sequence of various partition patterns via boosting, the BRSP classifier is more robust to the intra-class variations hence leads to a more
accurate classification. Both RSP-based classifiers are tested on three publicly available scene datasets. The experimental results highlight the effectiveness of the proposed methods. |
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