Performance evaluation of Decision Tree and neural network techniques for road scene image classification task

Abstract

This paper discusses the evaluation of two supervised learning based image classification algorithms. The classification subject of this work is part of a complete vision based road sign recognition system to be implanted using the VHDL language on an FPGA card for driver assistance applications. The classification is used in order to classify road scene images into different day times according to scene illumination and weather conditions. Due to the sensitivity of colors to illumination variation, the classification task is developed to improve the red color segmentation task which presents an important level in the road sign recognition system. In order to achieve real-time processing tasks and to reduce computing time and hardware resources occupation, the performance of the two predictive modeling techniques which are Neural Networks and Decision Trees is evaluated in this work. The VHDL circuit of the Decision Tree classifier is presented as well.

Scene Recognition using Significant Feature Detection Technique

“…As the name indicate scale invariant feature transform and satisfied the properties like robustness, accuracy and illumination changes. It is useful for finding a particular part of an image [11][12][13][14].…”

“…Convolutional layers can extricate the nearby highlights since they limit the responsive fields of the concealed layers to be neighbourhood. Theenhanced system structures of CNNs lead to saving in memory necessities and reducedcalculation complexity and, in the meantime, give better execution for applications where the info has nearby connection (e.g., picture and speech) [11][12][13][14].…”