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Erschienen in: Neural Computing and Applications 2/2019

05.07.2017 | Original Article

Object detection using hybridization of static and dynamic feature spaces and its exploitation by ensemble classification

verfasst von: Iqbal Murtza, Asifullah Khan, Naeem Akhtar

Erschienen in: Neural Computing and Applications | Ausgabe 2/2019

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Abstract

This paper presents a learning mechanism based on hybridization of static and dynamic learning. Realizing the detection performances offered by the state-of-the-art deep learning techniques and the competitive performances offered by the conventional static learning techniques, we propose the idea of exploitation of the concatenated (parallel) hybridization of the static and dynamic learning-based feature spaces. This is contrary to the cascaded (series) hybridization topology in which the initial feature space (provided by the conventional, static, and handcrafted feature extraction technique) is explored using deep, dynamic, and automated learning technique. Consequently, the characteristics already suppressed by the conventional representation cannot be explored by the dynamic learning technique. Instead, the proposed technique combines the conventional static and deep dynamic representation in concatenated (parallel) topology to generate an information-rich hybrid feature space. Thus, this hybrid feature space may aggregate the good characteristics of both conventional and deep representations, which are then explored using an appropriate classification technique. We also hypothesize that ensemble classification may better exploit this parallel hybrid perspective of the feature spaces. For this purpose, pyramid histogram of oriented gradients-based static learning has been incorporated in conjunction with convolution neural network-based deep learning to produce concatenated hybrid feature space. This hybrid space is then explored with various state-of-the-art ensemble classification techniques. We have considered the publicly available INRIA person and Caltech pedestrian standard image datasets to assess the performance of the proposed hybrid learning system. Furthermore, McNemar’s test has been used to statistically validate the outperformance of the proposed technique over various contemporary techniques. The validated experimental results show that the employment of the proposed hybrid representation results in effective detection performance (an AUC of 0.9996 for INRIA person and 0.9985 for Caltech pedestrian datasets) as compared to the individual static and dynamic representations.

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Fußnoten
1
In the context of this paper, conventional, static, and handcrafted are interchangeably used. Likewise, the deep, dynamic, and automated are also interchangeably used.
 
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Metadaten
Titel
Object detection using hybridization of static and dynamic feature spaces and its exploitation by ensemble classification
verfasst von
Iqbal Murtza
Asifullah Khan
Naeem Akhtar
Publikationsdatum
05.07.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3050-4

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