Database Retrieval: The Use of Combined Dissimilarities

In image retrieval systems, the key point is the description of the set of images. In this paper we show that a representation using a cloud of points offers a flexible description but suffers from class overlap. We propose a novel approach for describing clouds of points based on the support vector data description (SVDD). We show that combining image descriptions using dissimilarities improves the retrieval precision. Further we propose a method to select an efficient and robust subset of classifiers. We investigate the performance of the proposed retrieval technique on a database of 368 images

Handwritten digit recognition by combined classifiers

summary:Classifiers can be combined to reduce classification errors. We did experiments on a data set consisting of different sets of features of handwritten digits. Different types of classifiers were trained on these feature sets. The performances of these classifiers and combination rules were tested. The best results were acquired with the mean, median and product combination rules. The product was best for combining linear classifiers, the median for $k$-NN classifiers. Training a classifier on all features did not result in less errors