13,879 research outputs found
Multiscale Discriminant Saliency for Visual Attention
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between center and surround
classes. Discriminant power of features for the classification is measured as
mutual information between features and two classes distribution. The estimated
discrepancy of two feature classes very much depends on considered scale
levels; then, multi-scale structure and discriminant power are integrated by
employing discrete wavelet features and Hidden markov tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, saliency value for
each dyadic square at each scale level is computed with discriminant power
principle and the MAP. Finally, across multiple scales is integrated the final
saliency map by an information maximization rule. Both standard quantitative
tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating
the proposed multiscale discriminant saliency method (MDIS) against the
well-know information-based saliency method AIM on its Bruce Database wity
eye-tracking data. Simulation results are presented and analyzed to verify the
validity of MDIS as well as point out its disadvantages for further research
direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio
Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between centre and surround
classes. Discriminant power of features for the classification is measured as
mutual information between distributions of image features and corresponding
classes . As the estimated discrepancy very much depends on considered scale
level, multi-scale structure and discriminant power are integrated by employing
discrete wavelet features and Hidden Markov Tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, a saliency value for
each square block at each scale level is computed with discriminant power
principle. Finally, across multiple scales is integrated the final saliency map
by an information maximization rule. Both standard quantitative tools such as
NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed
multi-scale discriminant saliency (MDIS) method against the well-know
information based approach AIM on its released image collection with
eye-tracking data. Simulation results are presented and analysed to verify the
validity of MDIS as well as point out its limitation for further research
direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396
Object Classification in Astronomical Multi-Color Surveys
We present a photometric method for identifying stars, galaxies and quasars
in multi-color surveys, which uses a library of >65000 color templates. The
method aims for extracting the information content of object colors in a
statistically correct way and performs a classification as well as a redshift
estimation for galaxies and quasars in a unified approach. For the redshift
estimation, we use an advanced version of the MEV estimator which determines
the redshift error from the redshift dependent probability density function.
The method was originally developed for the CADIS survey, where we checked
its performance by spectroscopy. The method provides high reliability (6 errors
among 151 objects with R<24), especially for quasar selection, and redshifts
accurate within sigma ~ 0.03 for galaxies and sigma ~ 0.1 for quasars.
We compare a few model surveys using the same telescope time but different
sets of broad-band and medium-band filters. Their performance is investigated
by Monte-Carlo simulations as well as by analytic evaluation in terms of
classification and redshift estimation. In practice, medium-band surveys show
superior performance. Finally, we discuss the relevance of color calibration
and derive important conclusions for the issues of library design and choice of
filters. The calibration accuracy poses strong constraints on an accurate
classification, and is most critical for surveys with few, broad and deeply
exposed filters, but less severe for many, narrow and less deep filters.Comment: 21 pages including 10 figures. Accepted for publication in Astronomy
& Astrophysic
Large Margin Image Set Representation and Classification
In this paper, we propose a novel image set representation and classification
method by maximizing the margin of image sets. The margin of an image set is
defined as the difference of the distance to its nearest image set from
different classes and the distance to its nearest image set of the same class.
By modeling the image sets by using both their image samples and their affine
hull models, and maximizing the margins of the images sets, the image set
representation parameter learning problem is formulated as an minimization
problem, which is further optimized by an expectation -maximization (EM)
strategy with accelerated proximal gradient (APG) optimization in an iterative
algorithm. To classify a given test image set, we assign it to the class which
could provide the largest margin. Experiments on two applications of
video-sequence-based face recognition demonstrate that the proposed method
significantly outperforms state-of-the-art image set classification methods in
terms of both effectiveness and efficiency
A Review of Codebook Models in Patch-Based Visual Object Recognition
The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods
Mental state estimation for brain-computer interfaces
Mental state estimation is potentially useful for the development of asynchronous brain-computer interfaces. In this study, four mental states have been identified and decoded from the electrocorticograms (ECoGs) of six epileptic patients, engaged in a memory reach task. A novel signal analysis technique has been applied to high-dimensional, statistically sparse ECoGs recorded by a large number of electrodes. The strength of the proposed technique lies in its ability to jointly extract spatial and temporal patterns, responsible for encoding mental state differences. As such, the technique offers a systematic way of analyzing the spatiotemporal aspects of brain information processing and may be applicable to a wide range of spatiotemporal neurophysiological signals
Reliable ABC model choice via random forests
Approximate Bayesian computation (ABC) methods provide an elaborate approach
to Bayesian inference on complex models, including model choice. Both
theoretical arguments and simulation experiments indicate, however, that model
posterior probabilities may be poorly evaluated by standard ABC techniques. We
propose a novel approach based on a machine learning tool named random forests
to conduct selection among the highly complex models covered by ABC algorithms.
We thus modify the way Bayesian model selection is both understood and
operated, in that we rephrase the inferential goal as a classification problem,
first predicting the model that best fits the data with random forests and
postponing the approximation of the posterior probability of the predicted MAP
for a second stage also relying on random forests. Compared with earlier
implementations of ABC model choice, the ABC random forest approach offers
several potential improvements: (i) it often has a larger discriminative power
among the competing models, (ii) it is more robust against the number and
choice of statistics summarizing the data, (iii) the computing effort is
drastically reduced (with a gain in computation efficiency of at least fifty),
and (iv) it includes an approximation of the posterior probability of the
selected model. The call to random forests will undoubtedly extend the range of
size of datasets and complexity of models that ABC can handle. We illustrate
the power of this novel methodology by analyzing controlled experiments as well
as genuine population genetics datasets. The proposed methodologies are
implemented in the R package abcrf available on the CRAN.Comment: 39 pages, 15 figures, 6 table
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