NDLI: A Novel Method for Efficient Indoor–Outdoor Image Classification

Content Provider	Springer Nature Link
Author	Kim, Wonjun Park, Jimin Kim, Changick
Copyright Year	2010
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.
Starting Page	251
Ending Page	258
Page Count	8
File Format	PDF
ISSN	19398018
Journal	Journal of Signal Processing Systems
Volume Number	61
Issue Number	3
e-ISSN	19398115
Language	English
Publisher	Springer US
Publisher Date	2010-02-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Indoor–outdoor classification Low-level features Edge and color orientation histogram SVM classifier Computer Imaging, Vision, Pattern Recognition and Graphics Pattern Recognition Image Processing and Computer Vision Electrical Engineering Circuits and Systems Signal, Image and Speech Processing
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Signal Processing Control and Systems Engineering Information Systems Modeling and Simulation Hardware and Architecture

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