Indoor-outdoor detector for mobile phone cameras using gentle boosting

We developed a new compact indoor-outdoor detector suitable for an embedded digital camera in a mobile phone. The detector works on a Bayer domain image before applying white balance gains. The key idea is to use a small number of photometrical and colorimetrical features typically calculated in the mobile phone cameras for white balance gains evaluation. These features are collected using an annotated image database that was captured using the camera for a variety of indoor and outdoor scenes by different customers. Using this database, a gentle boosting classifier for indoor-outdoor detection is designed and evaluated. An optimal feature subset and optimal number of rounds are selected as well. On a set of 3,176 images, the proposed detector achieves a 1.7% error rate for indoor and 10.8% for outdoor scenes. A comparative study versus a number of known embedded indoor-outdoor detectors shows advantages of the proposed indoor-outdoor detector for mobile phone cameras.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[2]  Jiebo Luo,et al.  Image transform bootstrapping and its applications to semantic scene classification , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[4]  Raimondo Schettini,et al.  Consensus-based framework for illuminant chromaticity estimation , 2008, J. Electronic Imaging.

[5]  Antonio Torralba,et al.  Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.

[6]  Raimondo Schettini,et al.  Automatic Classification Of Digital Photographs Based On Decision Forests , 2004, Int. J. Pattern Recognit. Artif. Intell..

[7]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

[9]  Raimondo Schettini,et al.  Improving Color Constancy Using Indoor–Outdoor Image Classification , 2008, IEEE Transactions on Image Processing.

[10]  Jiebo Luo,et al.  Pictures Are Not Taken in a Vacuum , 2006 .

[11]  Martin Szummer,et al.  Indoor-outdoor image classification , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[12]  Brian V. Funt,et al.  A Large Image Database for Color Constancy Research , 2003, CIC.

[13]  Mingjing Li,et al.  Boosting image orientation detection with indoor vs. outdoor classification , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[14]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .