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In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented...
Speech with various emotions aggravates the performance of speaker recognition system. The existing speaker modeling disregards the match of the emotional state between training and testing speech, and the systems suffer the lapsus of the emotion recognition as to practical application. We propose an alternative approach that exploits the prosodic difference to cluster affective speech, and then builds...
A novel approach based on the probabilistic latent semantic analysis model (pLSA) for automatic musical genre classification is proposed in this paper. Unlike traditional usage, the pLSA is used to model musical genre instead of single music signal in the proposed approach. First, an unsupervised clustering algorithm is utilized to group temporal segments in music signals into several natural clusters...
Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks are generated from the region clusters of low level features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this paper, we supervise the clustering process by using the orientation information assigned to each interest point of...
Lots of growing neural network models have been proposed to tackle the incremental learning problem, but they also bring about the problem of fast growing complex structure. In this paper, we present a combinational Neural Network of SOM (Self-Organizing Maps) and RBF (Radial Basis Function) based on incremental learning method. The experiment of acoustic fault sources identification of underwater...
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