22,227 research outputs found
An Enhanced Method For Evaluating Automatic Video Summaries
Evaluation of automatic video summaries is a challenging problem. In the past
years, some evaluation methods are presented that utilize only a single feature
like color feature to detect similarity between automatic video summaries and
ground-truth user summaries. One of the drawbacks of using a single feature is
that sometimes it gives a false similarity detection which makes the assessment
of the quality of the generated video summary less perceptual and not accurate.
In this paper, a novel method for evaluating automatic video summaries is
presented. This method is based on comparing automatic video summaries
generated by video summarization techniques with ground-truth user summaries.
The objective of this evaluation method is to quantify the quality of video
summaries, and allow comparing different video summarization techniques
utilizing both color and texture features of the video frames and using the
Bhattacharya distance as a dissimilarity measure due to its advantages. Our
Experiments show that the proposed evaluation method overcomes the drawbacks of
other methods and gives a more perceptual evaluation of the quality of the
automatic video summaries.Comment: This paper has been withdrawn by the author due to some errors and
incomplete stud
Handwritten Digits Recognition using Deep Convolutional Neural Network: An Experimental Study using EBlearn
In this paper, results of an experimental study of a deep convolution neural
network architecture which can classify different handwritten digits using
EBLearn library are reported. The purpose of this neural network is to classify
input images into 10 different classes or digits (0-9) and to explore new
findings. The input dataset used consists of digits images of size 32X32 in
grayscale (MNIST dataset).Comment: This paper has been withdrawn by the author due to some errors and
incomplete stud
Exactly Solvable Balanced Tenable Urns with Random Entries via the Analytic Methodology
This paper develops an analytic theory for the study of some Polya urns with
random rules. The idea is to extend the isomorphism theorem in Flajolet et al.
(2006), which connects deterministic balanced urns to a differential system for
the generating function. The methodology is based upon adaptation of operators
and use of a weighted probability generating function. Systems of differential
equations are developed, and when they can be solved, they lead to
characterization of the exact distributions underlying the urn evolution. We
give a few illustrative examples.Comment: 23rd International Meeting on Probabilistic, Combinatorial, and
Asymptotic Methods for the Analysis of Algorithms (AofA'12), Montreal :
Canada (2012
The oscillatory distribution of distances in random tries
We investigate \Delta_n, the distance between randomly selected pairs of
nodes among n keys in a random trie, which is a kind of digital tree.
Analytical techniques, such as the Mellin transform and an excursion between
poissonization and depoissonization, capture small fluctuations in the mean and
variance of these random distances. The mean increases logarithmically in the
number of keys, but curiously enough the variance remains O(1), as n\to\infty.
It is demonstrated that the centered random variable
\Delta_n^*=\Delta_n-\lfloor2\log_2n\rfloor does not have a limit distribution,
but rather oscillates between two distributions.Comment: Published at http://dx.doi.org/10.1214/105051605000000106 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
Hard Decision Cooperative Spectrum Sensing Based on Estimating the Noise Uncertainty Factor
Spectrum Sensing (SS) is one of the most challenging issues in Cognitive
Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance
the detection reliability of a Primary User (PU) in fading environments. In
this paper, we propose a hard decision based CSS algorithm using energy
detection with taking into account the noise uncertainty effect. In the
proposed algorithm, two dynamic thresholds are toggled based on predicting the
current PU activity, which can be successfully expected using a simple
successive averaging process with time. Also, their values are evaluated using
an estimated value of the noise uncertainty factor. These dynamic thresholds
are used to compensate the noise uncertainty effect and increase (decrease) the
probability of detection (false alarm), respectively. Theoretical analysis is
performed on the proposed algorithm to deduce its enhanced false alarm and
detection probabilities compared to the conventional hard decision CSS.
Moreover, simulation analysis is used to confirm the theoretical claims and
prove the high performance of the proposed scheme compared to the conventional
CSS using different fusion rules.Comment: 5 pages, 4 figures, IEEE International Conference on Computer
Engineering and Systems (ICCES 2015). arXiv admin note: text overlap with
arXiv:1505.0558
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