279 research outputs found
Online Feature Selection for Visual Tracking
Object tracking is one of the most important tasks in many applications of computer vision. Many tracking methods use a fixed set of features ignoring that appearance of a target object may change drastically due to intrinsic and extrinsic factors. The ability to dynamically identify discriminative features would help in handling the appearance variability by improving tracking performance. The contribution of this work is threefold. Firstly, this paper presents a collection of several modern feature selection approaches selected among filter, embedded, and wrapper methods. Secondly, we provide extensive tests regarding the classification task intended to explore the strengths and weaknesses of the proposed methods with the goal to identify the right candidates for online tracking. Finally, we show how feature selection mechanisms can be successfully employed for ranking the features used by a tracking system, maintaining high frame rates. In particular, feature selection mounted on the Adaptive Color Tracking (ACT) system operates at over 110 FPS. This work demonstrates the importance of feature selection in online and realtime applications, resulted in what is clearly a very impressive performance, our solutions improve by 3% up to 7% the baseline ACT while providing superior results compared to 29 state-of-the-art tracking methods
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A person’s
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the user’s experience than generic
parameters, accurate predictions reveal important aspects of user’s
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumer’s buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five users’
personality factors and 1,200 personal users’ pictures
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
In an era where accumulating data is easy and storing it inexpensive, feature
selection plays a central role in helping to reduce the high-dimensionality of
huge amounts of otherwise meaningless data. In this paper, we propose a
graph-based method for feature selection that ranks features by identifying the
most important ones into arbitrary set of cues. Mapping the problem on an
affinity graph-where features are the nodes-the solution is given by assessing
the importance of nodes through some indicators of centrality, in particular,
the Eigen-vector Centrality (EC). The gist of EC is to estimate the importance
of a feature as a function of the importance of its neighbors. Ranking central
nodes individuates candidate features, which turn out to be effective from a
classification point of view, as proved by a thoroughly experimental section.
Our approach has been tested on 7 diverse datasets from recent literature
(e.g., biological data and object recognition, among others), and compared
against filter, embedded and wrappers methods. The results are remarkable in
terms of accuracy, stability and low execution time.Comment: Preprint version - Lecture Notes in Computer Science - Springer 201
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern recognition problems are by nature ranking problems. The main
objective of a ranking algorithm is to sort objects according to some criteria,
so that, the most relevant items will appear early in the produced result list.
Ranking methods can be analyzed from two different methodological perspectives:
ranking to learn and learning to rank. The former aims at studying methods and
techniques to sort objects for improving the accuracy of a machine learning
model. Enhancing a model performance can be challenging at times. For example,
in pattern classification tasks, different data representations can complicate
and hide the different explanatory factors of variation behind the data. In
particular, hand-crafted features contain many cues that are either redundant
or irrelevant, which turn out to reduce the overall accuracy of the classifier.
In such a case feature selection is used, that, by producing ranked lists of
features, helps to filter out the unwanted information. Moreover, in real-time
systems (e.g., visual trackers) ranking approaches are used as optimization
procedures which improve the robustness of the system that deals with the high
variability of the image streams that change over time. The other way around,
learning to rank is necessary in the construction of ranking models for
information retrieval, biometric authentication, re-identification, and
recommender systems. In this context, the ranking model's purpose is to sort
objects according to their degrees of relevance, importance, or preference as
defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with
arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern recognition problems are by nature ranking problems. The main
objective of a ranking algorithm is to sort objects according to some criteria,
so that, the most relevant items will appear early in the produced result list.
Ranking methods can be analyzed from two different methodological perspectives:
ranking to learn and learning to rank. The former aims at studying methods and
techniques to sort objects for improving the accuracy of a machine learning
model. Enhancing a model performance can be challenging at times. For example,
in pattern classification tasks, different data representations can complicate
and hide the different explanatory factors of variation behind the data. In
particular, hand-crafted features contain many cues that are either redundant
or irrelevant, which turn out to reduce the overall accuracy of the classifier.
In such a case feature selection is used, that, by producing ranked lists of
features, helps to filter out the unwanted information. Moreover, in real-time
systems (e.g., visual trackers) ranking approaches are used as optimization
procedures which improve the robustness of the system that deals with the high
variability of the image streams that change over time. The other way around,
learning to rank is necessary in the construction of ranking models for
information retrieval, biometric authentication, re-identification, and
recommender systems. In this context, the ranking model's purpose is to sort
objects according to their degrees of relevance, importance, or preference as
defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with
arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
Feature selection is playing an increasingly significant role with respect to
many computer vision applications spanning from object recognition to visual
object tracking. However, most of the recent solutions in feature selection are
not robust across different and heterogeneous set of data. In this paper, we
address this issue proposing a robust probabilistic latent graph-based feature
selection algorithm that performs the ranking step while considering all the
possible subsets of features, as paths on a graph, bypassing the combinatorial
problem analytically. An appealing characteristic of the approach is that it
aims to discover an abstraction behind low-level sensory data, that is,
relevancy. Relevancy is modelled as a latent variable in a PLSA-inspired
generative process that allows the investigation of the importance of a feature
when injected into an arbitrary set of cues. The proposed method has been
tested on ten diverse benchmarks, and compared against eleven state of the art
feature selection methods. Results show that the proposed approach attains the
highest performance levels across many different scenarios and difficulties,
thereby confirming its strong robustness while setting a new state of the art
in feature selection domain.Comment: Accepted at the IEEE International Conference on Computer Vision
(ICCV), 2017, Venice. Preprint cop
Infinite Feature Selection on Shore-Based Biomarkers Reveals Connectivity Modulation after Stroke
Connectomics is gaining increasing interest in the scientific and clinical communities. It consists in deriving models of structural or functional brain connections based on some local measures. Here we focus on structural connectivity as detected by diffusion MRI. Connectivity matrices are derived from microstructural indices obtained by the 3D-SHORE. Typically, graphs are derived from connectivity matrices and used for inferring node properties that allow identifying those nodes that play a prominent role in the network. This information can then be used to detect network modulations induced by diseases. In this paper we take a complementary approach and focus on link as opposed to node properties. We hypothesize that network modulation can be better described by measuring the connectivity alteration directly in the form of modulation of the properties of white matter fiber bundles constituting the network communication backbone. The goal of this paper is to detect the paths that are most altered by the pathology by exploiting a feature selection paradigm. Temporal changes on connection weights are treated as features and those playing a leading role in a patient versus healthy controls classification task are detected by the Infinite Feature Selection (Inf-FS) method. Results show that connection paths with high discriminative power can be identified that are shared by the considered microstructural descriptors allowing a classification accuracy ranging between 83% and 89%
"En Finlandia, la educación es un servicio público"
El país escandinavo lidera todas las mediciones mundiales de calidad educativa. La capacitación docente y la idea de igualdad de oportunidades que toda la sociedad comparte son los pilares de un sistema tan exitoso
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