Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper WeBCT8.15
Spatial String Matching for Image Classification
Liu, Yunqiang, Barcelona Media - Innovation Center
Caselles, Vicent, Univ. Pompeu Fabra
This paper presents a spatial string matching method to incorporate spatial information into the bag-of-words model, which
represents an image as an unordered distribution of local features. Spatial constraints among neighboring features are explored
in order to achieve better discrimination power for image classification. The features from neighboring points are
combined together and taken as a spatial string, and then our method matches the images according to the similarity of
string pairs. The categorization problem can be formulated using KNN or SVM classifier based on the spatial string matching
kernel. The proposed method is able to capture spatial dependencies across the neighboring features. Experiment
results show promising performance for image classification tasks.
13:30-16:30, Paper WeBCT8.16
A Semi-Supervised Gaussian Mixture Model for Image Segmentation
Martínez-Usó, Adolfo, Univ. Jaume I
Pla, F., Univ. Jaume I
Martínez Sotoca, Jose, Univ. Jaume I
In this paper, the results of a semi-supervised approach based on the Expectation-Maximisation algorithm for model-based
clustering are presented. We show in this work that, if the appropriate generative model is chosen, the classification accuracy
on clustering for image segmentation can be significantly improved by the combination of a reduced set of labelled
data and a large set of unlabelled data. This technique has been tested on real images as well as on medical images from
a dermatology application. The preliminary results are quite promising. Not only the unsupervised accuracies have been
improved as expected but the segmentation results obtained are considerably better than the results obtained by other powerful
and well-known unsupervised image segmentation techniques.
13:30-16:30, Paper WeBCT8.17
Adding Classes Online in Error Correcting Output Codes Framework
Escalera, Sergio, UB
Masip, David, CVC, UOC
Puertas, Eloi, Univ. de Barcelona
Radeva, Petia, CVC
Pujol, Oriol, UB
This article proposes a general extension of the Error Correcting Output Codes (ECOC) framework to the online learning
scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. Validation
on UCI database and two real machine vision applications show that the online problem-dependent ECOC proposal
provides a feasible and robust way for handling new classes using any base classifier.
13:30-16:30, Paper WeBCT8.18
Training Multi-Level Features for the RobotVision@ICPR 2010 Challenge
Sebastien, Paris, Univ. de la Méditerranée
Herve, Glotin, LSIS
This paper combines and proposes two novel multi-level spatial pyramidal (sp) features: spELBP (Extended Local Binary
Pattern), spELBOP (Extended Local Binary Orientation Pattern) and spHOEE (Histogram of Oriented Edge Energy).
These features feed state-of-the-art SVM algorithms for the localization of a robot in indoor environments. Two tasks are
associated with the RobotVision@ICPR 2010 Challenge, the first one uses only a frame of stereoscopic images, the second
takes into account the dynamics of the robot for improving results. Our scores are ranked 3rd for Task1 and 1st for Task2
13:30-16:30, Paper WeBCT8.19
Subclass Error Correcting Output Codes using Fisher’s Linear Discriminant Ratio
Arvanitopoulos, Nikolaos, Aristotle Univ. of Thessaloniki
Bouzas, Dimitrios, Aristotle Univ. of Thessaloniki
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