The week's pick
Reseach Article
Recognition and Detection of Objects from Images: An Approach using CSLBP Feature Extractor and CNN
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 16 |
| Year of Publication: 2020 |
| Authors: Mini Rani, Ankur Gupta |
10.5120/ijca2020920109
|
Mini Rani, Ankur Gupta . Recognition and Detection of Objects from Images: An Approach using CSLBP Feature Extractor and CNN. International Journal of Computer Applications. 176, 16 ( Apr 2020), 25-30. DOI=10.5120/ijca2020920109
Abstract
Recognition and identification for groups are two aspects of object recognition. Class recognition aims at classifying an object into one of many predefined categories. The detection goal is to distinguish objects from the background. There are growing difficulties in identifying objects including background removal and object detection etc. In this paper, a new approach is proposed for object recognition using the CNN. For feature extraction of the input data CSLBP and LPQ were used and then concatenated. These features were then used to train the convolutional neural network. Fifteen categories from Caltech101 dataset that contains 101 categories of images are considered in this research. The results states that the model achieved higher accuracy of 99% for almost all the 15 categories of the images that were considered. Thus, it can be said that the proposed model shows the efficiency of network in recognizing the objects correctly.
References
- Rani, M., & Gupta, A. 2019. Recognition and Detection of Multiple Objects from Images: A Review. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 8(12).
- L. Yan, Y. Wang, T. Song, and Z. Yin. 2017. An incremental intelligent object recognition system based on deep learning. Chinese Automation Congress (CAC), pp. 7135–7138.
- R. Bhuvaneswari and R. Subban. 2018. Novel object detection and recognition system based on points of interest selection and SVM classification. Cognitive Systems Research, vol. 52, pp. 985–994.
- C. M. Sukanya, R. Gokul, and V. Paul. 2016. A survey on object recognition methods. International Journal of Science, Engineering and Computer Technology, vol. 6, no. 1, pp. 48–48.
- A. Cheddad, H. Kusetogullari, and H. Grahn. 2017. Object recognition using shape growth pattern. Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, pp. 47–52.
- J. Tang and G. Wen, “Object recognition via classifier interaction with multiple features,” 8th International Conference on Intelligent Human- Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 337–340, 2016.
- H. Wu, R. Bie, J. Guo, X. Meng, and C. Zhang, “CNN refinement based object recognition through optimized segmentation,” Optik, vol. 150, pp. 76–82, 2017.
- Q. Guo, F. Wang, J. Lei, D. Tu, and G. Li, “Convolutional feature learning and Hybrid CNN-HMM for scene number recognition,” Neurocomputing, vol. 184, pp. 78–90, 2016.
- G. Ciocca, P. Napoletano, and R. Schettini, “CNN-based features for retrieval and classification of food images,” Computer Vision and Image Understanding, vol. 176, pp. 70–77, 2018.
- W. Hao, R. Bie, J. Guo, X. Meng, and S. Wang, “Optimized CNN based image recognition through target region selection,” Optik, vol. 156, pp. 772–777, 2018.
- Kumar, Sandeep, Aman Balyan, and Manvi Chawla. "Object detection and recognition in images." Int. J. Eng. Dev. Res.(IJEDR) 5, no. 4, pp. 1029-1034, 2017.
- Rahim, Md Abdur, Md Shafiul Azam, Nazmul Hossain, and Md Rashedul Islam. "Face recognition using local binary patterns (LBP)." Global Journal of Computer Science and Technology (2013).
- Satpathy, Amit, Xudong Jiang, and How-Lung Eng. "LBP-based edge-texture features for object recognition." IEEE Transactions on Image Processing 23, no. 5 (2014): 1953-1964.
- Baber, Junaid, Maheen Bakhtyar, Ihsan Ullah, Mariam Rehman, and Muhammad Khalid. "Effective Compression of Center Symmetric Local Binary Pattern." Mehran University Research Journal of Engineering and Technology 36, no. 2 (2017): 209-224.
- Zhang, E. H., Y. C. Li, and J. H. Duan. "Moving object detection based on confidence factor and CSLBP features." The Imaging Science Journal 64, no. 5 (2016): 253-261.
- Caltech101. (2006). Computational Vision: [Home]. https://www.vision.caltech.edu/Image_Datasets/Caltech101/#Description
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