Empirical evaluation of distance measures for supervised classification of remotely sensed image with Modified Multivariate Local Binary Pattern

Jenicka, S.; Suruliandi, A.

doi:10.1109/icetect.2011.5760220

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…The number of components, which corresponds to the intervals of intensities taken into account, is a parameter to be defined using a training set, for example. With this approach, the degree of similarity between the reference and target images is obtained by comparing their respective histograms using an adapted measure, like the Bhattacharyya distance [20] or X 2 (Chi-Square) [15].…”

Section: Color Histogrammentioning

confidence: 99%

Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques

Benarab

Napoléon

Alfalou

et al. 2015

Optics Communications

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Section: Color Histogrammentioning

confidence: 99%

Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques

Benarab

Napoléon

Alfalou

et al. 2015

Optics Communications

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“…9 The tracking process is based on the Mean-Shift algorithm 7 which allows to dene the region of interest in which we compare the histograms of the reference image and the target image.…”

Section: Histogram Based Approachesmentioning

confidence: 99%

A novel multitracking system for the evaluation of high-level swimmers performances

Benarab¹,

Napoléon²,

Alfalou³

et al. 2014

SPIE Proceedings

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Swimmer tracking has specic diculties compared to the other tracking systems due to some complex problems such as occlusion (by another person, a wall or splashing), variability of the target (in appearance, lighting or behavior). For the sake of conceiving a robust swimmer tracking system we started by developing mono-tracking systems based on some well-known pattern recognition techniques such as optical correlation (Non-linear JTC (Joint Transform Correlator)) and histogram based approaches (Color histogram, LBP (Local Binary Patterns) and HOG (Histogram of Oriented Gradients)). As an enhancement to these systems, we introduce the aspect of multi-tracking. Its basic idea is to track several potential targets which will give us several tracks then we choose the best track and relaunch the multi-tracking process. Forasmuch each technique has its own periods of good tracking and dropouts (loss of the object to be tracked), we propose a novel heterogeneous multi-tracking system by taking advantage of dierent tracking techniques. Each track represents an independent mono-tracking system. The use of the proposed heterogeneous multi-tracking system has improved signicantly the tracking results from 81.34% to 94%.

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“…Examples on such situations where a considerable better classification accuracy have been obtained by applying some other distance measure have been reported in several articles i.e. [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [31]. However, typically in these types of studies one has simply tested with a few different distance measure for classifying the data set at hand.…”

Section: Introductionmentioning

confidence: 98%

Differential evolution classifier with optimized distance measures from a pool of distances

Koloseni

Lampinen

Luukka

2012

2012 IEEE Congress on Evolutionary Computation

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In this article we propose a differential evolution based nearest prototype classifier with extension to selecting the applied distance measure from a pool of alternative measures optimally for the particular data set at hand. The proposed method extends the earlier differential evolution based nearest prototype classifier by extending the optimization process to cover also the selection of distance measure instead of optimizing only the parameters related with a preselected and fixed distance measure. Now the optimization process is seeking also for the best distance measure providing the highest classification accuracy over the selected data set. It has been clear for some time that in classification, the usual euclidean distance measure is sometimes not the best possible choice. Still usually not much has been done for it, and in many cases where some consideration to this problem is given, there has only been testing with a couple of alternative distance measures to find which one provides the highest classification accuracy over the current data set. In this paper we attempt to take one step further by not only enumerating a couple of alternative distance measures, but applying a systematic optimization process to select the best distance measure from a pool of multiple alternative distance measures. In parallel, within the same optimization process, the optimal parameter values related to each alternative distance measures are determined as well as the optimal class prototype vectors for the given data. The empirical results represented are indicating that with several data sets the optimal distance measure is some other measure than the most commonly applied euclidean distance. The results are also suggesting that from the classification accuracy point of view the proposed global optimization approach has high potential in solving classification problems of the studied type. Perhaps the most generally applicable conclusion from our results is, that emphasizing of selection of distance measure is more important to classification accuracy that it has been commonly believed so far.

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Empirical evaluation of distance measures for supervised classification of remotely sensed image with Modified Multivariate Local Binary Pattern

Cited by 10 publications

References 3 publications

Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques

Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques

A novel multitracking system for the evaluation of high-level swimmers performances

Differential evolution classifier with optimized distance measures from a pool of distances

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