Deep Feedforward Neural Network Learning Using Local Binary Patterns Histograms for Outdoor Object Categorization

Deep Feedforward Neural Network Learning Using Local Binary Patterns Histograms for Outdoor Object Categorization

Heni Bouhamed* | Yassine Ruichek

Advanced Technologies for Image and Signal Processing unit, Technopole of Sfax, 3018, Tunisia

Le2i FRE2005, CNRS, Arts et Métiers, University Bourgogne Franche-Comté, UTBM, Belfort F 90010, France

Corresponding Author Email: 
heni.bouhamed@fsegs.usf.tn
Page: 
158-162
|
DOI: 
https://doi.org/10.18280/ama_b.610309
Received: 
4 July 2018
|
Accepted: 
25 August 2018
|
Published: 
30 September 2018
| Citation

OPEN ACCESS

Abstract: 

Advanced driver assistance systems and outdoor video surveillance very often need to classify the detected objects/obstacles. In this context several works have presented and have tested some graph-based methods. Motivated by the prominence of deep neural networks, which surpass the performance of the previous dominating paradigm, we are going to apply him in the classification of images by using the local binary pattern (LBP) histograms, to our knowledge, our work is the only one to propose this conduct. We go to see that the results are very promising besides the fact that the construction of such a model is possible also in a massive data context.

Keywords: 

deep learning, deep feedforward neural network, local binary pattern histogram, classification

1. Introduction
2. Deep Feedforward Deep Neural Network (DFFNN)
3. Local Binary Patterns
4. Methodologies and Performance Evaluation
5. Conclusions
  References

[1] Albusac J, Castro-Schez JJ, López-López LM, Vallejo D, Jimenez-Linares L. (2009). A supervised learning approach to automate the acquisition of knowledge in surveillance systems. Signal Processing 89(12): 2400-2414.

[2] Bengio Y. (2009). Learning deep architectures for AI. Foundations and trends® in Machine Learning 2(1): 1-127.

[3] Cheng B, Yang J, Yan S, Fu Y, Huang TS. (2010). Learning With $\ell^{1} $-Graph for Image Analysis. IEEE Transactions on Image Processing 19(4): 858-866.

[4] Cireşan DC, Meier U, Gambardella LM, Schmidhuber J. (2010). Deep, big, simple neural nets for handwritten digit recognition. Neural Computation 22(12): 3207-3220.

[5] Dornaika F, Bosaghzadeh A, Salmane H, Ruichek Y. (2014). A graph construction method using LBP self-representativeness for outdoor object categorization. Engineering Applications of Artificial Intelligence 36: 294-302.

[6] Dornaika F, Bosaghzadeh A, Salmane H, Ruichek Y. (2014). Graph-based semi-supervised learning with Local Binary Patterns for holistic object categorization. Expert Systems with Applications 41(17): 7744-7753.

[7] Dornaika F, Bosaghzadeh A. (2015). Adaptive graph construction using data self-representativeness for pattern classification. Information Sciences 325: 118-139.

[8] Dornaika F, Moujahid A, El Merabet Y, Ruichek Y. (2016). Building detection from orthophotos using a machine learning approach: An empirical study on image segmentation and descriptors. Expert Systems with Applications 58: 130-142.

[9] Geronimo D, Lopez AM, Sappa AD, Graf T. (2010). Survey of pedestrian detection for advanced driver assistance systems. IEEE transactions on pattern analysis and machine intelligence 32(7): 1239-1258.

[10] Goodfellow I, Bengio Y, Courville A, Bengio Y. (2016). Deep learning. Cambridge: MIT press 1.

[11] Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, Kingsbury B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine 29(6): 82-97.

[12] Huang D, Shan C, Ardabilian M, Wang Y, Chen L. (2011). Local binary patterns and its application to facial image analysis: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41(6): 765-781.

[13] Jebara T, Wang J, Chang SF. (2009). Graph construction and b-matching for semi-supervised learning. In Proceedings of the 26th annual international conference on machine learning 441-448. ACM.

[14] Kim K, Chalidabhongse TH, Harwood D, Davis L. (2005). Real-time foreground–background segmentation using codebook model. Real-time imaging 11(3): 172-185.

[15] Kim W, Kim C. (2012). Background subtraction for dynamic texture scenes using fuzzy color histograms. IEEE Signal processing letters 19(3): 127-130.

[16] Lopez-Moreno I, Gonzalez-Dominguez J, Martinez D, Plchot O, Gonzalez-Rodriguez J, Moreno PJ. (2016). On the use of deep feedforward neural networks for automatic language identification. Computer Speech & Language 40: 46-59.

[17] Mohamed AR, Dahl GE, Hinton G. (2012). Acoustic modeling using deep belief networks. IEEE Trans. Audio, Speech & Language Processing 20(1): 14-22.

[18] Pan P, Schonfeld D. (2011). Visual tracking using high-order particle filtering. IEEE Signal Processing Letters 18(1): 51-54.

[19] Yu D, Deng L. (2011). Deep learning and its applications to signal and information processing [exploratory dsp]. IEEE Signal Processing Magazine 28(1): 145-154.

[20] Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y. (2009). Robust face recognition via sparse representation. IEEE transactions on pattern analysis and machine intelligence 31(2): 210-227.

[21] Zeiler MD, Ranzato M, Monga R, Mao M, Yang K, Le QV, Hinton GE. (2013). On rectified linear units for speech processing. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on 3517-3521. IEEE.

[22] Zikopoulos P, Eaton C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.