In this paper, we propose an improved version of RBF network based on Evidence Theory (NNET) using one input layer and two hidden layers and one output layer, to improve classifier combination and recognition reliability in particular for automatic semantic-based video content indexing and retrieval. Many combination schemes have been proposed in the literature according to the type of informat...

There have been many recent efforts in contentbased retrieval to perform automatic classification of images/visual objects. Most approaches, however, have focused on using individual classifiers. In this paper, we study the way in which, in a dynamic framework, multiple classifiers can be combined when applying Visual Object Detectors. We propose a hybrid classifier combination approach, in whi...

Classifier fusion approaches are receiving increasing attention for their capability of improving classification performances. At present, the usual operation mechanism for classifier fusion is the “combination” of classifier outputs. Improvements in performances are related to the degree of “error diversity” among combined classifiers. Unfortunately, in remote-sensing image recognition applica...

This research employs free model that uses only sentential features without paragraph context to extract topic sentences of a paragraph. For finding optimal combination of features, corpus-based classification is used for constructing a sentence classifier as the model. The sentence classifier is trained by using Support Vector Machine (SVM). The experiment shows that position and meta-discours...

Developing handwriting recognition systems that are fast and highly reliable is a challenging problem that has become increasingly relevant following the broad acceptance of hand-held devices that use pen based handwriting inputs. Generative classifiers offer a promising solution in combination with discriminative classifiers for addressing this challenge. This paper describes a novel generativ...

This paper mainly analyses the fault tolerant capability of a combining classifier that uses a K-voting strategy for integrating binary classifiers. From the point view of fault tolerance, we discuss the influence of the failure of binary classifiers on the final output of the combining classifier, and present a theoretical analysis of combination performance under three fault models. The resul...

In this paper we tackle the problem of unconstrained handwritten character recognition using different classification strategies. For such an aim, four multilayer perceptron classifiers (MLP) are built and used into three different classification strategies: combination of two 26– class classifiers; a 26–metaclass classifier and a 52– class classifier. Experimental results on the NIST SD19 data...

Combination functions typically used in biometric identification systems consider as input parameters only the matching scores related to a single person in order to derive a combined score for that person. We present a systematic framework to use all scores received by all persons as input to a single combination function when sufficient training data is available. More fundamentally, we ident...