2,583 research outputs found
One-Class-at-a-Time Removal Sequence Planning Method for Multiclass Classification Problems
Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods
Kernel and Classifier Level Fusion for Image Classification.
Automatic understanding of visual information is one of the main requirements for a complete artificial intelligence system and an essential component of autonomous robots. State-of-the-art image recognition approaches are based on different local descriptors, each capturing some properties of the image such as intensity, color and texture. Each set of local descriptors is represented by a codebook and gives rise to a separate feature channel. For classification the feature channels are combined by using multiple kernel learning (MKL), early fusion or classifier level fusion approaches. Due to the importance of complementary information in fusion techniques, there is an increasing demand for diverse feature channels. The first part of the thesis focuses on the ways to encode information from images that is complementary to the state-of-the-art local features. To address this issue we present a novel image representation which can encode the structure of an object and propose three descriptors based on this representation. In the state-of-the-art recognition system the kernels are often computed independently of each other and thus may be highly informative yet redundant. Proper selection and fusion of the kernels is, therefore, crucial to maximize the performance and to address the efficiency issues in visual recognition applications. We address this issue in second part of the thesis where, we propose novel techniques to fuse feature channels for object and pattern recognition. We present an extensive evaluation of the fusion methods on four object recognition datasets and achieve state-of-the-art results on all of them. We also present results on four bioinformatics datasets to demonstrate that the proposed fusion methods work for a variety of pattern recognition problems, provided that we have multiple feature channels
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks
PAC-Bayesian Majority Vote for Late Classifier Fusion
A lot of attention has been devoted to multimedia indexing over the past few
years. In the literature, we often consider two kinds of fusion schemes: The
early fusion and the late fusion. In this paper we focus on late classifier
fusion, where one combines the scores of each modality at the decision level.
To tackle this problem, we investigate a recent and elegant well-founded
quadratic program named MinCq coming from the Machine Learning PAC-Bayes
theory. MinCq looks for the weighted combination, over a set of real-valued
functions seen as voters, leading to the lowest misclassification rate, while
making use of the voters' diversity. We provide evidence that this method is
naturally adapted to late fusion procedure. We propose an extension of MinCq by
adding an order- preserving pairwise loss for ranking, helping to improve Mean
Averaged Precision measure. We confirm the good behavior of the MinCq-based
fusion approaches with experiments on a real image benchmark.Comment: 7 pages, Research repor
An In-Depth Study on Open-Set Camera Model Identification
Camera model identification refers to the problem of linking a picture to the
camera model used to shoot it. As this might be an enabling factor in different
forensic applications to single out possible suspects (e.g., detecting the
author of child abuse or terrorist propaganda material), many accurate camera
model attribution methods have been developed in the literature. One of their
main drawbacks, however, is the typical closed-set assumption of the problem.
This means that an investigated photograph is always assigned to one camera
model within a set of known ones present during investigation, i.e., training
time, and the fact that the picture can come from a completely unrelated camera
model during actual testing is usually ignored. Under realistic conditions, it
is not possible to assume that every picture under analysis belongs to one of
the available camera models. To deal with this issue, in this paper, we present
the first in-depth study on the possibility of solving the camera model
identification problem in open-set scenarios. Given a photograph, we aim at
detecting whether it comes from one of the known camera models of interest or
from an unknown one. We compare different feature extraction algorithms and
classifiers specially targeting open-set recognition. We also evaluate possible
open-set training protocols that can be applied along with any open-set
classifier, observing that a simple of those alternatives obtains best results.
Thorough testing on independent datasets shows that it is possible to leverage
a recently proposed convolutional neural network as feature extractor paired
with a properly trained open-set classifier aiming at solving the open-set
camera model attribution problem even to small-scale image patches, improving
over state-of-the-art available solutions.Comment: Published through IEEE Access journa
Contributions on distance-based algorithms, multi-classifier construction and pairwise classification
179 p.Aurkezten den ikerketa lan honetan saikapen atazak landu dira, non helburua,sailkapen gainbegiratuaren artearen-egoera aberastea izan den. Sailkapengainbegiratuaren zenbait estrategi analizatu dira, beraien ezaugarri etaahuleziak aztertuz. Beraz, ezaugarri positiboak mantenduz, ahuleziak hobetzekosaiakera egin da. Hau burutu ahal izateko, sailkapen gainbegiratuarenzenbait estrategi konbinatzeaz gain, zenbait bilaketa heuristiko ere erabili dira.Sailkapen gainbegiratuko 3 ikerketa lerro desberdinetan burutu dira ekarpenak.Aurkezten diren lehenengo proposamenak, K-NN algoritmoan zentratzendira, honen zenbait bertsio aurkezten direlarik. Ondoren sailkatzaileen konbinaketarekinerlazionatutako beste lan bat aurkezten da. Eta azkenik, binakakosailkapenaren zenbait estrategi berritzaile proposatzen dira. Ekarpenhauek aldizkari edo konferentzi internazionaletan publikatuak edo bidaliakizan dira.Buruturiko experimentuetan, proposatutako algoritmoak artearen-estatuanaurkituriko zenbait algoritmorekin konparatu dira, emaitza interesgarriak lortuaz.Honetaz gain, emaitza hauetatik ondorio esanguratsuak eskuratzeko asmoz,test estatistikoen erabilera ere burutu da
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