[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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268 H. Josiński et al. selected attributes to number of attributes in an input space. It can be noticed that greedy hill climbing gives much more compact descriptors with lower number of features in comparison to genetic search. For instance in case of variation cover Q=0.50 which gives 504 MPCA components, greedy hill climbing selects 7 and 34 features with wrapper and CFS evaluators respectively and genetic search selects 194 and 78 components. The difference rises with increasing variation cover Q. In case of Q=0.80 and 10773 MPCA components greedy hill climbing selects 10 and 240 features and genetic search selects 1746 and 1903 components. It is so because of the complexity of considered selection problem. The input set is very high dimensional and discriminative properties are scattered across MPCA components. It is extremely difficult to improve the classification accuracy by adding a single attribute, because only specified subsets of features have distinctive abilities. It is a reason why greedy hill climbing with wrapper subset evaluator terminates at first local extreme and it not able to discover efficiently individual gait features. Fig. 9. Compression rates However much more important assessment of selected MPCA components gives classification accuracy presented in Fig. 10. It shows percentage of correctly classified gaits of a test set in respect to MPCA variation cover Q and applied selection strategy. Globally best precision is 95%, obtained by 449 attributes selected from the input set containing 29400 MPCA components for Q=0.87, which means 0.03% of compression rate, by hill climbing search and CFS evaluator. Genetic search with CFS evaluator is very similar. It has 92.5% of classification precision and it has very similar dependency on a number of features of an input space. Much worse results gives wrapper evaluator, especially if combined with hill climbing search, which is caused by the same reason as explained in compression rates analysis.
Selection of Individual Gait Features Extracted by MPCA 269 Fig. 10. Selection rates 8 Conclusions The general conclusion are consistent with those presented in [6]. Multliinear principal component analysis is an effective method of feature extraction of a motion data, which allows to identify humans precisely. However individual gait features are scattered across numerous MPCA components. Thus attribute selection simplifies gait description, discovers most discriminative features and allows for more efficient classification. The identification performed on the basis of reduced MPCA space, as presented in Fig. 10 is improved significantly in comparing to raw MPCA space as presented in Fig. 6. What is more, although strong dimensionality reduction, selected attributes preserve most of individual gait features. The obtained features subsets strongly depends on utilized subset evaluation strategy. Wrapper evaluation approach which in general is considered to be more precise, in our problem gives significantly worse results. It can be explained by scattering of individual gait features across MPCA components, so correlation based measures as for instance CFS evaluator, are more efficient in the input space exploration. Acknowledgment. The work has been supported by The Polish National Science Centre as project number UMO-2011/01/B/ST6/06988. References [1] Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley (1989) [2] Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand (1998)
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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8 A. Babiarz et al. a) b) Fig. 5. T
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10 A. Babiarz et al. o o o “micvo
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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Recognition and Location of Objects
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Omnidirectional Video Acquisition D
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184 D. Bereska et al. 4.1 Mechanica
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186 D. Bereska et al. 4.1.3 Open-Lo
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Probabilistic Approach to Planning
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206 Practical Applications of Class
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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