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A Novel Classification Approach Capable Indoor Scene Picture Identification with Hybrid Feature Selection Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 40 |
| Year of Publication: 2018 |
| Authors: GaganDeep Singh, Sonika Jindal |
10.5120/ijca2018917055
|
GaganDeep Singh, Sonika Jindal . A Novel Classification Approach Capable Indoor Scene Picture Identification with Hybrid Feature Selection Algorithm. International Journal of Computer Applications. 180, 40 ( May 2018), 35-38. DOI=10.5120/ijca2018917055
Abstract
Image scene classification is an integral part of several aspects of image process. Indoor and outside classification could be a elementary part of scene process because it is that the place to begin of the many linguistics scene analysis approaches. Several novel techniques are developed to tackle this drawback, however every technique depends on its own information of pictures therefore reducing the boldness within the success of every technique. The planned model is formed capable of operating with the variations within the indoor scene image dataset, that are noticed within the sort of the color, texture, light, image orientation, occlusion and color illuminations. many experiments has been conducted over the projected model for the performance analysis of the indoor scene recognition system within the planned model. The results of the proposed model are obtained in the type of the various performance parameters of applied mathematics errors, precision, recall, F1-measure and overall accuracy. The planned technique has clearly outperformed the present models within the terms of the accuracy. The planned model improvement has been recorded above ten percent for all of the evaluated parameters against the prevailing models based mostly upon SURF, FREAK, etc.
References
- Li, Yansheng, et al. "Unsupervised multilayer feature learning for satellite image scene classification." IEEE Geoscience and Remote Sensing Letters 13.2 (2016): 157-161.
- Monadjemi, Amir, B. T. Thomas, and Majid Mirmehdi. Experiments on high resolution images towards outdoor scene classification. Technical report, University of Bristol, Department of Computer Science, 2002.
- Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons,, 1999.
- Fitzpatrick, Paul. "Indoor/outdoor scene classification project." Pattern Recognition and Analysis.
- Quattoni, Ariadna, and Antonio Torralba. "Recognizing indoor scenes." InComputer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 413-420. IEEE, 2009.
- Li, Li-Jia, Hao Su, Yongwhan Lim, and Li Fei-Fei. "Objects as attributes for scene classification." In Trends and Topics in Computer Vision, pp. 57-69. Springer Berlin Heidelberg, 2012.
- Antanas, Laura, Marco Hoffmann, Paolo Frasconi, Tinne Tuytelaars, and Luc De Raedt. "A relational kernel-based approach to scene classification." InApplications of Computer Vision (WACV), 2013 IEEE Workshop on, pp. 133-139. IEEE, 2013.
- Mesnil, Grégoire, Salah Rifai, Antoine Bordes, Xavier Glorot, Yoshua Bengio, and Pascal Vincent. "Unsupervised and Transfer Learning under Uncertainty-From Object Detections to Scene Categorization." In ICPRAM, pp. 345-354. 2013.
- Zhang, Lei, Xiantong Zhen, and Ling Shao. "Learning object-to-class kernels for scene classification." Image Processing, IEEE Transactions on 23, no. 8 (2014): 3241-3253.
- Li, Li-Jia, Hao Su, Li Fei-Fei, and Eric P. Xing. "Object bank: A high-level image representation for scene classification & semantic feature sparsification." In Advances in neural information processing systems, pp. 1378-1386. 2010.
- Alberti, Marina, John Folkesson, and Patric Jensfelt. "Relational approaches for joint object classification and scene similarity measurement in indoor environments." In AAAI 2014 Spring Symposia: Qualitative Representations for Robots.2014.
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