Neural Network Analysis of MINERVA Scene Analysis Benchmark

Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. MINERVA benchmark has been recently introduced in this area for testing different image processing and classification schemes. In this paper 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.

• Type: Conference
• Year: 2001
• Where: ICIAP
• Authors: Markos Markou, Sameer Singh, Mona Sharma
• Keywords: Feature Extraction Methods, Fold Cross-validation Task, ICIAP 2001, Neural Network