Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
A New Method for Indoor-outdoor Image Classification Using Color Correlated Temperature
Ali Nadian Ghomsheh, Alireza Talebpour
Pages - 167 - 181     |    Revised - 15-05-2012     |    Published - 20-06-2012
Volume - 6   Issue - 3    |    Publication Date - June 2012  Table of Contents
Indoor-outdoor Image Classification, Color Segmentation, Color Correlated Temperature
In this paper a new method for indoor-outdoor image classification is presented; where the concept of Color Correlated Temperature is used to extract distinguishing features between the two classes. In this process, using Hue color component, each image is segmented into different color channels and color correlated temperature is calculated for each channel. These values are then incorporated to build the image feature vector. Besides color temperature values, the feature vector also holds information about the color formation of the image. In the classification phase, KNN classifier is used to classify images as indoor or outdoor. Two different datasets are used for test purposes; a collection of images gathered from the internet and a second dataset built by frame extraction from different video sequences from one video capturing device. High classification rate, compared to other state of the art methods shows the ability of the proposed method for indoor-outdoor image classification.
CITED BY (4)  
1 Raja, R., Roomi, S., & Dharmalakshmi, D. (2015, January). Robust indoor/outdoor scene classification. In Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on (pp. 1-5). IEEE.
2 Yousef, A., Iftekharuddin, K. M., & Karim, M. A. (2014). Shoreline extraction from light detection and ranging digital elevation model data and aerial images. Optical Engineering, 53(1), 011006-011006.
3 Raja, R., Md Mansoor Roomi, S., Dharmalakshmi, D., & Rohini, S. (2013, December). Classification of indoor/outdoor scene. In Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on (pp. 1-4). IEEE.
4 Homola, T. (2013). Techniques for Content-Based Sub-Image Retrieval.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 Angadi, S.A. and M.M. Kodabagi, A Texture Based Methodology for Text Region Extraction from Low Resolution Natural Scene Images. International Journal of Image Processing, 2009. 3(5): p. 229-245.
2 Bianco, S., et al., Improving Color Constancy Using Indoor–Outdoor Image Classification. IEEE transaction on Image Processing, 2008. 17(12): p. 2381-2392.
3 Szummer, M. and R.W. Picard, Indoor-outdoor image classification, in IEEE Workshop on Content-Based Access of Image and Video Database. 1998: Bombay, India. p. 42- 51.
4 Ohta, Y.I., T. Kanade, and T. Sakai, Color information for region segmentation. Computer Graphics and Image Processing, 1980. 13(3): p. 222-241.
5 Serrano, N., A. Savakis, and J. Luo, A computationally efficient approach to indoor/outdoor scene classification, in International Conference on Pattern Recognition. 2002: QC, Canada, . p. 146-149.
6 Miene, A., et al., Automatic shot boundary detection and classification of indoor and outdoor scenes, in Information Technology, 11th Text Retrieval Conference. 2003, Citeseer. p. 615-620.
7 Qiu, G., X. Feng, and J. Fang, Compressing histogram representations for automatic colour photo categorization. Pattern Recognition, 2004. 37(11): p. 2177-2193.
8 Huang, J., et al., Image indexing using color correlograms. International Journal of Computer Vision, 2001: p. 245–268.
9 Qiu, G., Indexing chromatic and achromatic patterns for content-based colour image retrieval. Pattern Recognition, 2002. 35(8): p. 1675-1686.
10 Qiu, G. and K.M. Lam, Spectrally layered color indexing. Lecture Notes in Computer Science, 2002. 2384: p. 100-107.
11 Collier, J. and A. Ramirez-Serrano, Environment Classification for Indoor/Outdoor Robotic Mapping, in Canadian Conference on Computer and Robot Vision. 2009, IEEE. p. 276-283.
12 Boutell, M. and J. Luo, Bayesian Fusion of Camera Metadata Cues in Semantic Scene Classification, in Computer Vision and Pattern Recognition (CVPR). 2004: Washington, D.C. p. 623-630.
13 Tao, L., Y.H. Kim, and Y.T. Kim, An efficient neural network based indoor-outdoor scene classification algorithm, in International Conference on Consumer Electronics. 2010: Las Vegas, NV p. 317-318.
14 Vailaya, A., et al., Image classification for content-based indexing. IEEE Transactions on Image Processing, 2001. 10(1): p. 117-130.
15 Kim, W., J. Park, and C. Kim, A Novel Method for Efficient Indoor–Outdoor Image Classification. Signal Processing Systems, 2010. 61(3): p. 1-8.
16 Daubechies, I., Ten Lectures on Wavlets. 1992, Philadelphia: SIAM Publications.
17 Gupta, L., et al., Indoor versus outdoor scene classification using probabilistic neural network Eurasip Journal on Advances in Signal Processing, 2007. 1: p. 123-133.
18 Tolambiya, A., S. Venkatraman, and P.K. Kalra, Content-based image classification with wavelet relevance vector machines. Soft Computing, 2010. 14(2): p. 129-136.
19 Hu, G.H., J.J. Bu, and C. Chen, A novel Bayesian framework for indoor-outdoor image classification, in International Conference on Machine Learning and Cybernetics 2003: Xian. p. 3028-3032
20 Payne, A. and S. Singh, Indoor vs. outdoor scene classification in digital photographs. Pattern Recognition, 2005. 38(10): p. 1533-1545.
21 Songbo, T., An effective refinement strategy for KNN text classifier. Expert Systems with Application, 2006. 30(2): p. 290-298.
22 Shafer, S.A., Using color to separate reflection components. Color Research and Application, 1985. 10(4): p. 210.
23 Ebner, M., Color constancy. 2007, West Sussex: Wily.
24 Finlayson, G., G. Schaefer, and S. Surface, Single surface colour constancy, in 7th Color Imaging Conference: Color Science, Systems, and Applications. 1999: Scottsdale, USA. p. 106-113.
25 Cheng, H.D., et al., color image segmentation: advances and prospects. Pattern Recognition, 2001. 34(12): p. 2259-2281.
26 WNUKOWICZ, K. and W. SKARBEK, Colour temperature estimation algorithm for digital images - properties and convergence. OPTO-ELECTRONICS REVIEW, 2003. 11(3): p. 193-196.
27 Lee, H., et al., One-dimensional conversion of color temperature in perceived illumination. Consumer Electronics, IEEE Transactions on, 2001. 47(3): p. 340-346.
28 Javier, H., J. Romero, and L.R. Lee, Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities. Applied Optis, 1999. 38(27): p. 5703-5709.
29 Robertson, A. and R. Alan, computation of Correlated Color Temperature abd Distribution Temperature. Color Research and Application, 1968. 58(11): p. 1528-1535.
30 Yang, E. and L. Wang, Joint optimization of run-length coding, Huffman coding, and quantization table with complete baseline JPEG decoder compatibility. Image Processing, IEEE Transactions on, 2009. 18(1): p. 63-74.
Mr. Ali Nadian Ghomsheh
- Iran
Dr. Alireza Talebpour
- Iran