Pattern Recognition Letters

Items from 1 to 19 out of 19 results

article

Target detection of ISAR data by principal component transform on co-occurrence matrix

Mousumi Gupta, Debasish Bhaskar, Rabindranath Bera, Sambhunath Biswas

Pattern Recognition Letters > 2012 > 33 > 13 > 1682-1688

Issue of automated target detection in ISAR can be stated as what features enhance objects of interest from the rest of the data. Much experimentation done in this area have used Fourier transforms for preprocessing the raw signal data. Generally the ISAR data are comes with a matrix of complex number values and therefore intuitive logic appears to favor a Fourier transform. A hypothesis was made...

article

Automatic grayscale image colorization using histogram regression

Shiguang Liu, Xiang Zhang

Pattern Recognition Letters > 2012 > 33 > 13 > 1673-1681

Colorization aims to adding colors to a grayscale image. This task is ill-posed in the sense that assigning the colors to a grayscale image without any prior knowledge is ambiguous. Most of the previous methods require some amount of user interventions, making colorization a hard work. Motivated by this, a novel automatic grayscale image colorization method based on histogram regression is presented...

article

On the simultaneous recognition of identity and expression from BU-3DFE datasets

Yedatore V. Venkatesh, Ashraf A. Kassim, Jun Yuan, Tan Dat Nguyen

Pattern Recognition Letters > 2012 > 33 > 13 > 1785-1793

We propose a new linear model, based on resampling 3D face meshes to convert them to 3D matrices, to recognize identity and expression simultaneously. This contrasts with bilinear models currently used with 3D meshes. The matrices are amenable to algebraic operations for facial data analysis and synthesis. Facial emotion is represented as a linear combination of its identity and expression using principal...

article

Data mining from multiple heterogeneous relational databases using decision tree classification

Tahar Mehenni, Abdelouahab Moussaoui

Pattern Recognition Letters > 2012 > 33 > 13 > 1768-1775

Nowadays, the expansion of computer networks and the diversity of data sources require new data mining approaches in multi-database systems. We propose a classification approach across multiple heterogeneous relational databases. More specifically, given a set of inter-related databases, we use a regression model for predicting the most useful links that will be connected to build a multi-relational...

article

Component-based global k-NN classifier for small sample size problems

Nan Zhang, Jian Yang, Jian-jun Qian

Pattern Recognition Letters > 2012 > 33 > 13 > 1689-1694

The classical k-NN classifier has been widely used in pattern recognition. However, it does not take into account the structural information of local samples. This paper presents a novel classifier named component-based global k-NN classifier (CG-k-NN), which takes advantage of the structural information of the local neighbors for enhancing the classification performance. We choose k nearest neighbors...

article

In search of optimal centroids on data clustering using a binary search algorithm

Abdolreza Hatamlou

Pattern Recognition Letters > 2012 > 33 > 13 > 1756-1760

Data clustering is an important technique in data mining. It is a method of partitioning data into clusters, in which each cluster must have data of great similarity and different clusters must have data of high dissimilarity. A lot of clustering algorithms are found in the literature. In general, there is no single algorithm that is suitable for all types of data, conditions and applications. Each...

article

A novel efficient local illumination compensation method based on DCT in logarithm domain

Zhichao Lian, Meng Joo Er, Yanchun Liang

Pattern Recognition Letters > 2012 > 33 > 13 > 1725-1733

In this paper, a novel illumination compensation method for face recognition under illumination variations is proposed. Rather than performing illumination compensation in a global way, the proposed method uses low-frequency discrete cosine transform (DCT) coefficients in the logarithm domain to estimate illumination in local areas. To estimate the illumination of every point more precisely, a mean...

article

Efficient feature selection filters for high-dimensional data

Artur J. Ferreira, Mário A.T. Figueiredo

Pattern Recognition Letters > 2012 > 33 > 13 > 1794-1804

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be computationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional datasets.In this paper, we propose efficient...

article

Analysis of directional patterns of lung nodules in computerized tomography using Getis statistics and their accumulated forms as malignancy and benignity indicators

Stelmo Magalhães Barros Netto, Aristófanes Corrêa Silva, Rodolfo Acatauassú Nunes, Marcelo Gattass

Pattern Recognition Letters > 2012 > 33 > 13 > 1734-1740

The large incidence of lung cancer in Brazil and around the world, in addition to its difficult diagnosis, especially in the initial stages, has been driving efforts to develop tools that support image-based diagnosis. The main objective is to avoid invasive procedures, which usually pose risks to patients. This work uses Getis spatial autocorrelation statistics, Getis ^∗ , plus its accumulated...

article

Iterative random projections for high-dimensional data clustering

Ângelo Cardoso, Andreas Wichert

Pattern Recognition Letters > 2012 > 33 > 13 > 1749-1755

In this text we propose a method which efficiently performs clustering of high-dimensional data. The method builds on random projection and the K-means algorithm. The idea is to apply K-means several times, increasing the dimensionality of the data after each convergence of K-means. We compare the proposed algorithm on four high-dimensional datasets, image, text and two synthetic, with K-means clustering...

article

Feature selection from high-order tensorial data via sparse decomposition

Donghui Wang, Shu Kong

Pattern Recognition Letters > 2012 > 33 > 13 > 1695-1702

Principal component analysis (PCA) suffers from the fact that each principal component (PC) is a linear combination of all the original variables, thus it is difficult to interpret the results. For this reason, sparse PCA (sPCA), which produces modified PCs with sparse loadings, arises to clear away this interpretation puzzlement. However, as a result of that sPCA is limited in handling vector-represented...

article

Fast multi-scale local phase quantization histogram for face recognition

Zhen Lei, Stan Z. Li

Pattern Recognition Letters > 2012 > 33 > 13 > 1761-1767

Multi-scale local phase quantization (MLPQ) is an effective face descriptor for face recognition. In previous work, MLPQ is computed by using Short-term Fourier Transformation (SFT) in local regions and the high-dimension histogram based features are extracted for face representation. This paper tries to improve MLPQ based face recognition in terms of accuracy and efficiency. It has two main contributions...

article

Spatio-temporal reasoning for the classification of satellite image time series

François Petitjean, Camille Kurtz, Nicolas Passat, Pierre Gançarski

Pattern Recognition Letters > 2012 > 33 > 13 > 1805-1815

Satellite image time series (SITS) analysis is an important domain with various applications in land study. In the coming years, both high temporal and high spatial resolution SITS will become available. In the classical methodologies, SITS are studied by analyzing the radiometric evolution of the pixels with time. When dealing with high spatial resolution images, object-based approaches are generally...

article

Space–time spectral model for object detection in dynamic textured background

Imtiaz Ali, Julien Mille, Laure Tougne

Pattern Recognition Letters > 2012 > 33 > 13 > 1710-1716

Background models are used for object detection in many computer vision algorithms. In this article, we propose a novel background modeling method based on frequency for spatially varying and time repetitive textured background. The local Fourier transform is applied to construct a pixel-wise representation of local frequency components. We apply our method for object detection in moving background...

article

Algorithms for maximum-likelihood bandwidth selection in kernel density estimators

José M. Leiva-Murillo, Antonio Artés-Rodríguez

Pattern Recognition Letters > 2012 > 33 > 13 > 1717-1724

In machine learning and statistics, kernel density estimators are rarely used on multivariate data due to the difficulty of finding an appropriate kernel bandwidth to overcome overfitting. However, the recent advances on information-theoretic learning have revived the interest on these models. With this motivation, in this paper we revisit the classical statistical problem of data-driven bandwidth...

article

Editors-in-Chief, Area/Associate/Advisory/Founding Editors

Pattern Recognition Letters > 2012 > 33 > 13 > IFC

article

Testing for the absence of correlation between two spatial or temporal sequences

Ronny O. Vallejos

Pattern Recognition Letters > 2012 > 33 > 13 > 1741-1748

The purpose of this paper is to elucidate the problem of testing for the absence of correlation between the trajectories of two stochastic processes. It is assumed that the process is homogeneous on a pre-specified partition of the index set. The hypothesis testing methodology developed in this article consists in estimating codispersion coefficients on each subset of the partition, and in testing...

article

Gradient computation in linear-chain conditional random fields using the entropy message passing algorithm

Velimir M. Ilić, Dejan I. Mančev, Branimir T. Todorović, Miomir S. Stanković

Pattern Recognition Letters > 2012 > 33 > 13 > 1776-1784

The paper proposes a numerically stable recursive algorithm for the exact computation of the linear-chain conditional random field gradient. It operates as a forward algorithm over the log-domain expectation semiring and has the purpose of enhancing memory efficiency when applied to long observation sequences. Unlike the traditional algorithm based on the forward–backward recursions, the memory complexity...

article

Discriminative feature extraction for speech recognition using continuous output codes

Omid Dehzangi, Bin Ma, Eng Siong Chng, Haizhou Li

Pattern Recognition Letters > 2012 > 33 > 13 > 1703-1709

Feature transformation techniques have been widely investigated to reduce feature redundancy and to introduce additional discriminative information with the aim to improve the performance of automatic speech recognition (ASR). In this paper, we propose a novel method to obtain discriminative feature transformation based on output coding technique for speech recognition. The output coding transformation...

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Number:
13
Volume:
33
Year:
2012

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Keywords

FEATURE SELECTION (3)
FACE RECOGNITION (2)
HIGH-DIMENSIONAL DATA (2)
3D FACE RECOGNITION (1)
3D FACIAL EXPRESSION RECOGNITION (1)
A BINARY SEARCH ALGORITHM (1)
ADABOOST (1)
BACKGROUND MODEL (1)
BILINEAR MODEL (1)
BIOMETRICS (1)
CLASSIFICATION (1)
CLUSTERING (1)
CO-OCCURRENCE MATRIX (1)
CODISPERSION COEFFICIENT (1)
COLORIZATION (1)
COLORIZATION CONFIDENCE (1)
COMPUTER-AIDED DIAGNOSIS (CADX) (1)
CONDITIONAL RANDOM FIELDS (1)
COVARIANCE (1)
DATA CLUSTERING (1)
DATA MINING (1)
DCT (1)
DEFORMABLE MODELS (1)
DIMENSIONALITY REDUCTION (1)
DISPERSION MEASURES (1)
DYNAMIC TEXTURE (1)
EXPECTATION SEMIRING (1)
FEATURE TRANSFORMATION (1)
FILTERS (1)
FORWARD–BACKWARD ALGORITHM (1)
GENERALIZED DISCRIMINANT ANALYSIS (1)
GETIS∗ STATISTICS (1)
GRADIENT COMPUTATION (1)
GRAPHICAL MODELS (1)
HETEROGENEOUS RELATIONAL DATABASES (1)
HIGH-ORDER PRINCIPAL COMPONENT ANALYSIS (1)
HISTOGRAM REGRESSION (1)
HYPOTHESIS TESTING (1)
ILLUMINATION COMPENSATION (1)
IMAGE EDITING (1)
IMAGE PROCESSING (1)
INFORMATION EXTRACTION (1)
INTER-DATABASE LINKS (1)
ISAR (1)
K-MEANS (1)
K-NN CLASSIFIER (1)
KERNEL DENSITY ESTIMATION (1)
LINEAR REGRESSION (1)
LINK USEFULNESS (1)
LOCAL FOURIER TRANSFORM (1)
LOCAL PHASE QUANTIZATION (1)
LOGARITHM DOMAIN (1)
LRC (1)
LUNG NODULES (1)
MEAN ESTIMATION (1)
MEDICAL IMAGE (1)
MESSAGE PASSING (1)
MLPQ (1)
MULTI-DATABASE MINING (1)
MULTI-RELATIONAL CLASSIFICATION (1)
MULTI-TEMPORAL ANALYSIS (1)
MULTIVARIATE DENSITY MODELING (1)
OBJECT DETECTION (1)
OPTIMAL CENTROIDS (1)
OUTPUT CODING (1)
PATTERN CLASSIFICATION (1)
PATTERN RECOGNITION (1)
PCT (1)
RANDOM PROJECTIONS (1)
SATELLITE IMAGE TIME SERIES (1)
SEGMENTATION (1)
SIMILARITY MEASURES (1)
SPARSE PRINCIPAL COMPONENT ANALYSIS (1)
SPATIAL PROCESSES (1)
SPEECH RECOGNITION (1)
SRC (1)
SURVEILLENCE (1)
TARGET (1)
TENSOR DECOMPOSITION (1)
TIKHONOV REGULARIZATION (1)
TIME SERIES (1)
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Pattern Recognition Letters

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