Digital Image Processing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Boosting Image Orientation Detection with Indoor vs. Outdoor Classification
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Indoor versus outdoor scene classification using probabilistic neural network
EURASIP Journal on Applied Signal Processing
Indoor vs. outdoor scene classification in digital photographs
Pattern Recognition
Bayesian fusion of camera metadata cues in semantic scene classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image classification for content-based indexing
IEEE Transactions on Image Processing
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
IEEE Transactions on Image Processing
Improving Color Constancy Using Indoor–Outdoor Image Classification
IEEE Transactions on Image Processing
Real-time detection of landscape scenes
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Privacy-aware image classification and search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Describing video contents in natural language
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
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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.