Author: ANDRES EDUARDO GUTIERREZ RODRÍGUEZ
In clustering, providing an explanation of the results is an important task.
Pattern-based clustering algorithms provide, in addition to the list of objects
belonging to each cluster, an explanation of the results in terms of a set of
patterns that describe the objects grouped in each cluster. It makes these
algorithms very attractive from the practical point of view; however, patternbased
clustering algorithms commonly have a high computational cost in the
clustering stage. Moreover, the most recent algorithms proposed within this
approach, extract patterns from numerical datasets by applying an a priori
discretization process, which may cause information loss. In this thesis, we
propose new algorithms for extracting only a subset of patterns useful for
clustering, from a collection of diverse unsupervised decision trees induced
from a dataset. Additionally, we propose a new clustering algorithm based
on these patterns.
Improving fingerprint matching algorithms is an active and important research area in fingerprint recognition. Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle. To alleviate these drawbacks, we introduce in this paper a novel fingerprint matching algorithm, named M3gl. This algorithm contains three components: a new feature representation containing clockwise-arranged minutiae without a central minutia, a new similarity measure that shifts the triplets to find the best minutiae correspondence, and a global matching procedure that selects the alignment by maximizing the amount of global matching minutiae. To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation. In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases.
Luis Luis Pellegrin OCTAVIO LOYOLA GONZALEZ JOSE ORTIZ BEJAR MIGUEL ANGEL MEDINA PEREZ ANDRES EDUARDO GUTIERREZ RODRIGUEZ Eric Sadit Téllez Avila MARIO GRAFF GUERRERO SABINO MIRANDA JIMENEZ Daniela Moctezuma MAURICIO ALFONSO GARCIA LIMON ALICIA MORALES REYES CARLOS ALBERTO REYES GARCIA Eduardo Morales Manzanares Hugo Jair Escalante (2019)
This paper describes the design of the 2017 RedICA: Text-Image Matching (RICATIM) challenge, including the dataset generation, a complete analysis of results, and the descriptions of the top-ranked developed methods. The academic challenge explores the feasibility of a novel binary image classification scenario, where each instance corresponds to the concatenation of learned representations of an image and a word. Instances are labeled as positive if the word is relevant for describing the visual content of the image, and negative otherwise. This novel approach of the image classification problem poses an alternative scenario where any text-image pair can be represented in such space, so any word could be considered for describing an image. The proposed methods are diverse and competitive, showing considerable improvements over the proposed baselines.