2,309,646 research outputs found
Robot-aided cloth classification using depth information and CNNs
The final publication is available at link.springer.comWe present a system to deal with the problem of classifying garments from a pile of clothes. This system uses a robot arm to extract a garment and show it to a depth camera. Using only depth images of a partial view of the garment as input, a deep convolutional neural network has been trained to classify different types of garments.
The robot can rotate the garment along the vertical axis in order to provide different views of the garment to enlarge the prediction confidence and avoid confusions. In addition to obtaining very high classification scores, compared to previous approaches to cloth classification that match the sensed data against a database, our system provides a fast and occlusion-robust solution to the problem.Peer ReviewedPostprint (author's final draft
Distributed Online Big Data Classification Using Context Information
Distributed, online data mining systems have emerged as a result of
applications requiring analysis of large amounts of correlated and
high-dimensional data produced by multiple distributed data sources. We propose
a distributed online data classification framework where data is gathered by
distributed data sources and processed by a heterogeneous set of distributed
learners which learn online, at run-time, how to classify the different data
streams either by using their locally available classification functions or by
helping each other by classifying each other's data. Importantly, since the
data is gathered at different locations, sending the data to another learner to
process incurs additional costs such as delays, and hence this will be only
beneficial if the benefits obtained from a better classification will exceed
the costs. We model the problem of joint classification by the distributed and
heterogeneous learners from multiple data sources as a distributed contextual
bandit problem where each data is characterized by a specific context. We
develop a distributed online learning algorithm for which we can prove
sublinear regret. Compared to prior work in distributed online data mining, our
work is the first to provide analytic regret results characterizing the
performance of the proposed algorithm
Objective Classification of Galaxy Spectra using the Information Bottleneck Method
A new method for classification of galaxy spectra is presented, based on a
recently introduced information theoretical principle, the `Information
Bottleneck'. For any desired number of classes, galaxies are classified such
that the information content about the spectra is maximally preserved. The
result is classes of galaxies with similar spectra, where the similarity is
determined via a measure of information. We apply our method to approximately
6000 galaxy spectra from the ongoing 2dF redshift survey, and a mock-2dF
catalogue produced by a Cold Dark Matter-based semi-analytic model of galaxy
formation. We find a good match between the mean spectra of the classes found
in the data and in the models. For the mock catalogue, we find that the classes
produced by our algorithm form an intuitively sensible sequence in terms of
physical properties such as colour, star formation activity, morphology, and
internal velocity dispersion. We also show the correlation of the classes with
the projections resulting from a Principal Component Analysis.Comment: submitted to MNRAS, 17 pages, Latex, with 14 figures embedde
Texture Classification Using Information Theory
Visual texture is one of the most fundamental properties of a visible surface. It participates as one of the major modalities which help us in the understanding of our visual environment. The different textures in an image are usually very apparent to a human observer, but automatic description of these patterns has proved to be complex
A Study of SVM Kernel Functions for Sensitivity Classification Ensembles with POS Sequences
Freedom of Information (FOI) laws legislate that government documents should be opened to the public. However, many government documents contain sensitive information, such as confidential information, that is exempt from release. Therefore, government documents must be sensitivity reviewed prior to release, to identify and close any sensitive information. With the adoption of born-digital documents, such as email, there is a need for automatic sensitivity classification to assist digital sensitivity review. SVM classifiers and Part-of-Speech sequences have separately been shown to be promising for sensitivity classification. However, sequence classification methodologies, and specifically SVM kernel functions, have not been fully investigated for sensitivity classification. Therefore, in this work, we present an evaluation of five SVM kernel functions for sensitivity classification using POS sequences. Moreover, we show that an ensemble classifier that combines POS sequence classification with text classification can significantly improve sensitivity classification effectiveness (+6.09% F2) compared with a text classification baseline, according to McNemar's test of significance
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