Abstract book (pdf) - ICPR 2010

10:00-10:20, Paper WeAT5.4
Face Hallucination under an Image Decomposition Perspective
Liang, Yan, Sun Yat-sen Univ.
Lai, Jian-Huang, Sun Yat-sen Univ.
Xie, Xiaohua, Sun Yat-sen Univ.
Liu, Wanquan, Curtin Univ. of Tech.
In this paper we propose to convert the task of face hallucination into an image decomposition problem, and then use the
morphological component analysis (MCA) for hallucinating a single face image, based on a novel three-step framework.
Firstly, a low-resolution input image is up-sampled by interpolation. Then, the MCA is employed to decompose the interpolated
image into a high-resolution image and an unsharp masking, as MCA can properly decompose a signal into special
parts according to typical dictionaries. Finally, a residue compensation, which is based on the neighbor reconstruction of
patches, is performed to enhance the facial details. The proposed method can effectively exploit the facial properties for
face hallucination under the image decomposition perspective. Experimental results demonstrate the effectiveness of our
method, in terms of the visual quality of the hallucinated face images.
10:20-10:40, Paper WeAT5.5
Gender Classification using Local Directional Pattern (LDP)
Jabid, Taskeed, Kyung Hee Univ.
Kabir, Md. Hasanul, Kyung Hee Univ.
Chae, Oksam, Kyung Hee Univ.
In this paper, we present a novel texture descriptor Local Directional Pattern (LDP) to represent facial image for gender
classification. The face area is divided into small regions, from which LDP histograms are extracted and concatenated
into a single vector to efficiently represent the face image. The classification is performed by using support vector machines
(SVMs), which had been shown to be superior to traditional pattern classifiers in gender classification problem. Experimental
results show the superiority of the proposed method on the images collected from FERET face database and
achieved 95.05% accuracy.
WeAT6 Anadolu Auditorium
Document Analysis - I Regular Session
Session chair: Baird, Henry (Lehigh Univ.)
09:00-09:20, Paper WeAT6.1
Generating Sets of Classifiers for the Evaluation of Multi-Expert Systems
Impedovo, Donato, Pol. di Bari
Pirlo, Giuseppe, Univ. degli Studi di Bari
This paper addresses the problem of multi-classifier system evaluation by artificially generated classifiers. For the purpose,
a new technique is presented for the generation of sets of artificial abstract-level classifiers with different characteristics
at the individual-level (i.e. recognition performance) and at the collective-level (i.e. degree of similarity). The technique
has been used to generate sets of classifiers simulating different working conditions in which the performance of combination
methods can be estimated. The experimental tests demonstrate the effectiveness of the approach in generating simulated
data useful to investigate the performance of combination methods for abstract-level classifiers.
09:20-09:40, Paper WeAT6.2
Imbalance and Concentration in K-NN Classification
Yin, Dawei, Lehigh Univ.
An, Chang, Lehigh Univ.
Baird, Henry, Lehigh Univ.
We propose algorithms for ameliorating difficulties in fast approximate k Nearest Neighbors (kNN) classifiers that arise
from imbalances among classes in numbers of samples, and from concentrations of samples in small regions of feature
space. These problems can occur with a wide range of binning kNN algorithms such as k-D trees and our variant, hashed
k-D trees. The principal method we discuss automatically rebalances training data and estimates concentration in each Kd
hash bin separately, which then controls how many samples should be kept in each bin. We report an experiment on
- 165 -