As we know that the normalization is a pre-processing stage of any type problem statement. Especially normalization takes important role in the field of soft computing, cloud computing etc. for manipulation of data like scale down or scale up the range of data before it becomes used for further stage. There are so many normalization techniques are there namely Min-Max normalization, Z-score nor...

Merging the outputs of different search engines or information sources in response to a query has been shown to improve performance. In most cases, scores produced by different information sources are not comparable: merging techniques are often segregated into a score normalization step followed by a combination step. The Combination step is usually straight forward and has been an area of act...

Recognition problems in computer vision often benefit from 5 5 a fusion of different algorithms and/or sensors, with score level fusion be6 6 ing among the most widely used fusion approaches. Score level fusion re7 7 quires the different data to be normalized before combining. Choosing an 8 8 appropriate score normalization technique before fusion is a fundamen9 9 tally difficult problem becaus...

Information fusion at the matching score level is widely used, due to the simplicity in combining the scores generated by different matchers. Since the matching scores output by various modalities are diverse in numerical range, score normalization is needed first, to transform these scores into a common domain. Then score fusion is to be carried out on the normalized scores. In this paper, we ...

Seven common normalization strategies are compared for unstopped citation tone F0 in the Chinese dialects of Shanghai, Cantonese, Fúzhōu and Zhāngzhōu. A z-score normalization is shown to give clearly superior clustering as quantified by normalization index, but no indication of superiority for a prior log transform of F0 is found.

Recognition problems in computer vision often benefit from a fusion of different algorithms and/or sensors, with score level fusion being among the most widely used fusion approaches. Choosing an appropriate score normalization technique before fusion is a fundamentally difficult problem because of the disparate nature of the underlying distributions of scores for different sources of data. Fur...

State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions. We propose a novel parameter estimation method for score normalization based on logistic regression, using the expected parameters from past queries. Experiments on the Gov2 and CluewebA collection indicate that our method is consistently more precise in predicting the number of re...

In this paper we introduce a MAP estimation of speaker models in Automatic Speaker Verification with a distance constraint: the D-MAP adaptation. The D-MAP is based on the Kullback-Leibler distances and provides an easy way to automatically compute a speaker-dependent adaptation of the model parameters. We formulate a distance constrained MAP criterion and we show an equivalence between the D-M...

Among the various proposed score normalizations, Tand Z-norm are most widely used in speaker verification systems. The main idea in these normalizations is to reduce the variations in impostor scores in order to improve accuracy. These normalizations require selection of a set of cohort models or utterances in order to estimate the impostor score distribution. In this paper we investigate basin...

In this paper, we propose some novel normalization and fusion techniques for biometric matching score level fusion in person verification. While conventional matching score level fusion methods use global score statistics, we consider in this work both genuine and impostor statistics separately. Performing a joint mean normalization of the separate monomodal scores, multimodal scores with less ...