Title :
A new scene analysis using genetic algorithm based fuzzy ID3 method
Author :
Chang, Jyh-Yeong ; Cho, Chien-Wen
Author_Institution :
Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fDate :
31 July-4 Aug. 2005
Abstract :
In this paper, we utilize a neural network based machine learning algorithm to segment natural objects in outdoor scene images. We have developed a genetic algorithm based fuzzy ID3 method, which can build a fuzzy decision tree to summarize the regularities existing in the data set. Using this method, we then propose a road scene analysis system, by which natural element segmentation rules can be learned from several road scene images. In the image analysis phase, the natural element regions are obtained through inference on these learned rules. Moreover, we can apply image groundtruthing to further improve the classification accuracy. The testing results have demonstrated that the object segmentation accuracy is quite high.
Keywords :
decision trees; fuzzy set theory; genetic algorithms; image classification; image segmentation; learning (artificial intelligence); neural nets; object detection; roads; fuzzy ID3 method; fuzzy decision tree; genetic algorithm; image classification; image groundtruthing; machine learning; natural object segmentation; neural network; outdoor scene image; road scene analysis system; Decision trees; Fuzzy sets; Genetic algorithms; Image analysis; Image segmentation; Layout; Machine learning algorithms; Neural networks; Roads; Testing;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556148