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:
4 July 2018
| |
25 August 2018
| | Citation



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.


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

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