%0 Journal Article %1 2000-shanahan %A Shanahan, J. %A Thomas, B. %A Mirmehdi, M. %A Martin, T. %A Campbell, N. %A Baldwin, J. %D 2000 %J Journal of Intelligent and Robotic Systems %K algorithms, genetic programming %N 4 %P 349--387 %R doi:10.1023/A:1008158907779 %T A Soft Computing Approach to Road Classification %V 29 %X Current learning approaches to computer vision have mainly focused on low-level image processing and object recognition, while tending to ignore high-level processing such as understanding. Here we propose an approach to object recognition that facilitates the transition from recognition to understanding. The proposed approach embraces the synergistic spirit of soft computing, exploiting the global search powers of genetic programming to determine fuzzy probabilistic models. It begins by segmenting the images into regions using standard image processing approaches, which are subsequently classified using a discovered fuzzy Cartesian granule feature classifier. Understanding is made possible through the transparent and succinct nature of the discovered models. The recognition of roads in images is taken as an illustrative problem in the vision domain. The discovered fuzzy models while providing high levels of accuracy (97per cent), also provide understanding of the problem domain through the transparency of the learnt models. The learning step in the proposed approach is compared with other techniques such as decision trees, naive Bayes and neural networks using a variety of performance criteria such as accuracy, understandability and efficiency.

@article{2000-shanahan, abstract = {Current learning approaches to computer vision have mainly focused on low-level image processing and object recognition, while tending to ignore high-level processing such as understanding. Here we propose an approach to object recognition that facilitates the transition from recognition to understanding. The proposed approach embraces the synergistic spirit of soft computing, exploiting the global search powers of genetic programming to determine fuzzy probabilistic models. It begins by segmenting the images into regions using standard image processing approaches, which are subsequently classified using a discovered fuzzy Cartesian granule feature classifier. Understanding is made possible through the transparent and succinct nature of the discovered models. The recognition of roads in images is taken as an illustrative problem in the vision domain. The discovered fuzzy models while providing high levels of accuracy (97per cent), also provide understanding of the problem domain through the transparency of the learnt models. The learning step in the proposed approach is compared with other techniques such as decision trees, naive Bayes and neural networks using a variety of performance criteria such as accuracy, understandability and efficiency.}, abstract-url = {http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000525}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Shanahan, J. and Thomas, B. and Mirmehdi, M. and Martin, T. and Campbell, N. and Baldwin, J.}, biburl = {https://www.bibsonomy.org/bibtex/2ba048139ccccc6e85fbd4cf31cbd1db4/brazovayeye}, doi = {doi:10.1023/A:1008158907779}, interhash = {dd05c04abc5154d1c7f151bb95997e6f}, intrahash = {ba048139ccccc6e85fbd4cf31cbd1db4}, issn = {0921-0296}, journal = {Journal of Intelligent and Robotic Systems}, keywords = {algorithms, genetic programming}, month = {December}, notes = {Further Information.This paper is not on-line, please contact the author.}, number = 4, pages = {349--387}, pubtype = {101}, timestamp = {2008-06-19T17:51:33.000+0200}, title = {A Soft Computing Approach to Road Classification}, volume = 29, year = 2000 }