International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 11, No. 6, 8 Jun. 2019
Cover page and Table of Contents: PDF (size: 435KB)
Dimension Reduction using Orthogonal Local Preserving Projection in Big data
Full Text (PDF, 435KB), PP.69-77
Views: 0 Downloads: 0
Author(s)
Index Terms
Histogram of Oriented Gradients, Orthogonal Local Preserving Projection, Pedestrian, Principal Component Analysis, Support Vector Machine
Abstract
Big Data is unstructured data that overcome the processing complexity of conventional database systems. The dimensionality reduction approach, which is a fundamental technique for the large-scale data-processing, try to maintain the performance of the classifier while reduce the number of required features. The pedestrian data includes a number of features compare to the other data, so pedestrian detection is the complex task. The accuracy of detection and location directly affect the performance of the entire system. Moreover, the pedestrian based approaches mainly suffer from huge training samples and increase the computation complexity. In this paper, an efficient dimensionality reduction model and pedestrian data classification approach has been proposed. The proposed model has three steps Histogram of Oriented Gradients (HOG) descriptor used for feature extraction, Orthogonal Locality Preserving Projection (OLPP) approach for feature dimensionality reduction. Finally, the relevant features are forwarded to the Support Vector Machine (SVM) to classify the pedestrian data and non-pedestrian data. The proposed HOG+OLPP+SVM model performance was measured using evaluation metrics such as precision, accuracy, recall and f-measure. The proposed model used the Penn-Fudan Database and compare to the existing research the proposed model improved approximately 6% of pedestrian data classification accuracy.
Cite This Paper
Ummadi Sathish Kumar, E. Srinivasa Reddy, "Dimension Reduction using Orthogonal Local Preserving Projection in Big data", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.6, pp.69-77, 2019. DOI:10.5815/ijisa.2019.06.06
Reference
[1]K. Yang, E. J. Delp, and E. Du, “Categorization-based two-stage pedestrian detection system for naturalistic driving data,” Signal, Image and Video Processing, Vol. 8, No. 1, pp. 135-144, December 2014.
[2]C. G. Keller, and D. M. Gavrila, “Will the pedestrian cross? A study on pedestrian path prediction,” IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 2, pp. 494-506, April 2014.
[3]R. Sun, G. Zhang, X. Yan, and J. Gao, “Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation,” Sensors, Vol. 16, No. 8, pp. 1296, August 2016.
[4]M. Enzweiler, and D. M. Gavrila, “A multilevel mixture-of-experts framework for pedestrian classification,” IEEE Transactions on Image Processing, Vol. 20, No. 10, pp. 2967-2979, October 2011.
[5]C. B. Ng, Y. H. Tay, and B. M. Goi, “Pedestrian gender classification using combined global and local parts-based convolutional neural networks” Pattern Analysis and Applications, pp. 1-12, 2018.
[6]Y. Jiang, J. Wang, Y. Liang, and J. Xia, “Combining static and dynamic features for real-time moving pedestrian detection,” Multimedia Tools and Applications, pp.1-15, May 2018.
[7]R. M. Mueid, C. Ahmed, and M. A. R. Ahad, “Pedestrian activity classification using patterns of motion and histogram of oriented gradient,” Journal on Multimodal User Interfaces, Vol. 10, No. 4, pp. 299-305, July 2016.
[8]Y. Liu, L. Zeng, and Y. Huang, “An efficient HOG–ALBP feature for pedestrian detection,” Signal, Image and Video Processing, vol. 8, No. 1, pp.125-134, 2014.
[9]R. P. Yadav, V. Senthamilarasu, K. Kutty, and S. P. Ugale, “Implementation of robust HOG-SVM based pedestrian classification,” International Journal of Computer Applications, vol. 114, No. 19, January 2015.
[10]F. Meng, Z. Qi, Y. Tian, and L. Niu, “Pedestrian detection based on the privileged information,” Neural Computing and Applications, Vol. 29, pp. 1485-1494, June 2018.
[11]C. I. Orozco, M. E. Buemi, and J. J. Berlles, “New Deep Convolutional Neural Network Architecture for Pedestrian Detection,” In Proceeding of 8th International Conference of Pattern Recognition Systems (ICPRS 2017), pp. 1-6, July 2017.
[12]J. Li, X. Liang, S. Shen, T. Xu, J. Feng, and S. Yan, “Scale-aware fast R-CNN for pedestrian detection,” IEEE Transactions on Multimedia, Vol. 20, No. 4, pp. 985-996, April 2018.
[13]S. I. Jung, and K. S. Hong, “Deep network aided by guiding network for pedestrian detection,” Pattern Recognition Letters, Vol. 90, pp.43-49, April 2017.
[14]J. F. Kooij, G. Englebienne, and D. M. Gavrila, “Mixture of switching linear dynamics to discover behavior patterns in object tracks,” IEEE transactions on pattern analysis and machine intelligence, Vol. 38, No. 2, pp. 322-334, February 2016.
[15]X. Li, L. Li, F. Flohr, J. Wang, H. Xiong, M. Bernhard, S. Pan, D. M. Gavrila, and K. Li, “A unified framework for concurrent pedestrian and cyclist detection,” IEEE T INTELL TRANSP. vol. 18, pp. 269-281, February 2017.
[16]B. Chen, H. Sun, L. Feng, G. Xia, and G. Zhang, “Robust image compressive sensing based on m-estimator and nonlocal low-rank regularization,” Neurocomputing, Vol. 275, pp. 586-597, January 2018.
[17]A. Maronidis, E. Chatzilari, S. Nikolopoulos, and I. Kompatsiaris, “Scalable image annotation using a product compressive sampling approach”, In proceedings of IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1-10, October 2015.
[18]S. Zhu, B. Zeng, and M. Gabbouj, “Adaptive sampling for compressed sensing based image compression,” Journal of Visual Communication and Image Representation, Vol. 30, pp. 94-105, 2015.
[19]Y. K. Lee, E. R. Lee, and B. U. Park, “Principal component analysis in very high-dimensional spaces,” Statistica Sinica, pp. 933-956, 2012.
[20]H. Tian, Z. Duan, A. Abraham, and H. Liu, “A novel multiplex cascade classifier for pedestrian detection,” Pattern Recognition Letters, Vol. 34, No. 14, pp.1687-1693, October 2013.
[21]L. Kuang, L. T. Yang, J. Chen, F. Hao, and C. Luo, “A Holistic Approach for Distributed Dimensionality Reduction of Big Data,” IEEE Transactions on Cloud Computing, Vol. 2, pp. 506-518, 2018.
[22]M. Nasir, C. P. Lim, S. Nahavandi, and D. Creighton, “A genetic fuzzy system to model pedestrian walking path in a built environment,” Simulation Modelling Practice and Theory, Vol. 45, pp. 18-34, 2014.
[23]S. K. Choudhury, P. K. Sa, R. P. Padhy, S. Sharma, and S. Bakshi, “Improved pedestrian detection using motion segmentation and silhouette orientation,” Multimedia Tools and Applications, pp.1-40. 2017.
[24]L. Sun, X. Liang, and Q. Zhao, “Recursive Templates Segmentation and Exemplars Matching for Human Parsing,” The Computer Journal, vol. 57, No.3, pp. 364-377, 2014.
[25]R. Soundrapandiyan, and P. C. Mouli, “An Approach to Adaptive Pedestrian Detection and Classification in Infrared Images Based on Human Visual Mechanism and Support Vector Machine,” Arabian Journal for Science and Engineering, pp. 1-13, 2017.
[26]V. Gajjar, Y. Khandhediya, A. Gurnani, V. Mavani, M. S. Raval, M. Nakada, H. Chen, D. Terzopoulos, H. Hosseini, B. Xiao, and M. Jaiswal, “ViS-HuD: Using Visual Saliency to Improve Human Detection with Convolutional Neural Networks,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1908-1916, 2018.
[27]G. Khandelwal, V. Anandi, M. V. Deepak, V. N. Prasad, K. Manikantan, and F. Francis, “Pedestrian detection using single box convergence with iterative DCT based haar cascade detector and skin color segmentation,” In proceedings of IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 32-37, November 2015.
[28]R. Soundrapandiyan, and P. C. Mouli, “Adaptive pedestrian detection in infrared images using background subtraction and local thresholding,” Procedia Computer Science, vol. 58, pp.706-713, 2015.
[29]A. Halidou, X. You, M. Hamidine, R. A. Etoundi, and L. H. Diakite, “Fast pedestrian detection based on region of interest and multi-block local binary pattern descriptors,” Computers & Electrical Engineering, Vol. 40, No. 8, pp. 375-389, 2014.
[30]J. K. Kang, H. G. Hong, and K. R. Park, “Pedestrian detection based on adaptive selection of visible light or far-infrared light camera image by fuzzy inference system and convolutional neural network-based verification,” Sensors, vol. 17, No. 7, pp. 1598, 2017.