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A Novel Method for Efficient Indoor–Outdoor Image Classification

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Abstract

Most traditional indoor–outdoor scene classification approaches utilize the simple statistics of the low-level features, such as colors, edges, and textures. However, the existence of colors similar to sky or grass often yields the false positives. To cope with this deficiency, we focus on the orientation of low-level features in this paper. First, the image is partitioned into five block regions, whose features are differently weighted in the following classification stage according to the block positions. The edge and color orientation histogram (ECOH) descriptors are defined to represent each block efficiently. Finally, all ECOH values are concatenated to generate the feature vector and fed into the SVM classifier for the indoor–outdoor scene classification. To justify the efficiency and robustness of the proposed method, the evaluation is conducted over 1200 images.

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Acknowledgements

This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency NIPA-2010-(C1090-0902-0017)).

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Authors and Affiliations

  1. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 119 Munji street, Yuseong-gu, Daejeon, 305-714, South Korea

    Wonjun Kim, Jimin Park & Changick Kim

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  1. Wonjun Kim
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  2. Jimin Park
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  3. Changick Kim
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Correspondence to Changick Kim.

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Kim, W., Park, J. & Kim, C. A Novel Method for Efficient Indoor–Outdoor Image Classification. J Sign Process Syst 61, 251–258 (2010). https://doi.org/10.1007/s11265-009-0446-0

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