Due to its intuitive learning algorithms and classification behavior, learning vector quantization (LVQ) enjoys a wide popularity in diverse application domains. In recent years, the classical heuristic schemes have been accompanied by variants which can be motivated by a statistical framework such as robust soft LVQ (RSLVQ). In its original form, LVQ and RSLVQ can be applied to vectorial data ...

This paper presents some results on the possibilities offered by neural networks for human face recognition. In particular, two algorithms have been tested: learning vector quantization (LVQ) and multilayer perceptron (MLP). Two different approaches have been taken for each case, using as input data either preprocessed images (gray level or segmented), or geometrical features derived from a set...

Two approaches for ultrasonic image processing are examined. First, signal-adaptive maximum likelihood (SAML) filters are proposed for ultrasonic speckle removal. It is shown that in the case of displayed ultrasound (US) image data the maximum likelihood (ML) estimator of the original (noiseless) signal closely resembles the L(2) mean which has been proven earlier to be the ML estimator of the ...

Vocal and nonvocal segmentation is an important task in singing voice signal processing. Before identifying the singer it is necessary to locate the singer’s voice in a song. Maximum of the songs start with a piece of instrumental accompaniment known as ‘prelude’ in musical terms after which the singing voice comes into play. Therefore, it is necessary to detect the vocal region in the song in ...

The Multiple-Wordlength Operation Grouping (MWOG) is a recently used approach for an optimized implementation on a Field Programmable Gate Array (FPGA). By fixing the precision constraint, this approach allows minimizing the data wordlength. In this paper, the authors present the integration of the approach based on the MWOG in the Algorithm Architecture Adequation (AAA) methodology, designed t...

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn favorable feature representations for this special application: limited rankmatrix learning vector quanti...

Organizations as well as personal users invest a great deal of time in assigning documents they read or write to categories. Automatic document classification that matches user subjective classification is widely used, but much challenging research still remain to be done. The self-organizing map (SOM) is an artificial neural network (ANN) that is mathematically characterized by transforming hi...

This paper presents an axiomatic approach to soft learning vector quantization (LVQ) and clustering based on reformulation. The reformulation of the fuzzy c-means (FCM) algorithm provides the basis for reformulating entropy-constrained fuzzy clustering (ECFC) algorithms. This analysis indicates that minimization of admissible reformulation functions using gradient descent leads to a broad varie...

Digital watermarking techniques have been been largely applied into copyright protection and authentication of multimedia data. This paper proposes a novel digital watermarking algorithm for digital images by combining the hyperanalytic wavelet packet transform (HWPT) decomposition and Learning Vector Quantization (LVQ) neural network. For inserting watermark, the non-overlapping blocked origin...

This article presents a hybrid technique for the recognition of typed Arabic characters. Due to its curved and continuous nature, Arabic text has to go through words segmentation, character segmentation, feature extraction, and finally character recognition. In this work, Freeman Chain (FC) technique [20, 21] is used to generate a chain for every segmented character. This chain represents the e...