BibTeX
@MISC{Singh_neuralnetworks,
author = {Sameer Singh and Markos Markou and Maneesha Singh},
title = {Neural Networks for Scene Analysis},
year = {}
}
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Abstract
Neural networks have been widely regarded as powerful classifiers. They are particularly suited in scene analysis applicatio n areas where the task is to correctly label different image objects in a scene. Only after this stage is successful, one can build upon this information to generate a semantic understanding of the complete scene. In this paper we investigate the application of neural networks to the classification of natural objects in natural scenes available as a part of the publicly available MINERVA benchmark. The image processing component consists of image segmentation procedure that generates region definitions in images and texture algorithms that compute characteristic features from these. Our results are based on a total of 40 data sets generated using a combination of four segmentation algorithms and five grey scale texture extraction algorithms for classifying vegetation classes and natural object classes. In addition, on the best segmentation method, colour texture features based on correlograms and colour moments are investigated. The paper presents exhaustive results on these data sets and compares the utility of neural networks on a ten-fold cross-validation task.
Keyphrases
neural network scene analysis data set natural object class different image object complete scene colour moment natural object image segmentation procedure region definition natural scene segmentation algorithm semantic understanding grey scale texture extraction algorithm exhaustive result vegetation class compute characteristic feature colour texture feature powerful classifier available minerva benchmark scene analysis applicatio area segmentation method image processing component ten-fold cross-validation task
