Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. The MINERVA benchmark has recently been introduced in this area for testing different image processing and classification schemes. We present results on the classification of eight natural objects in the complete set of 448 natural images using neural networks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a ten fold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results.
Neural network analysis of MINERVA scene analysis benchmark
2001-01-01
782470 byte
Conference paper
Electronic Resource
English
Neural Network Analysis of MINERVA Scene Analysis Benchmark
British Library Conference Proceedings | 2001
|British Library Online Contents | 1999
|Engineering Index Backfile | 1924
Engineering Index Backfile | 1930
Online Contents | 2012