Cortex-inspired multilayer hierarchy based object detection system using PHOG descriptors and ensemble classification
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- 作者：Iqbal Murtza ; Duraid Abdullah ; Asifullah Khan ; Muhammad Arif…
- 关键词：Object detection ; Brain’s cortex ; Ensemble classification ; Feature extraction
- 刊名：The Visual Computer
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：33
- 期：1
- 页码：99-112
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision;
- 出版者：Springer Berlin Heidelberg
- ISSN：1432-2315
- 卷排序：33
文摘
In this paper, a hierarchal feature extraction and ensemble classification-based framework for object detection is proposed. The proposed object detection technique is motivated by the hierarchical learning mechanism in primate visual cortex, where each layer processes information differently. Initially, pyramid histogram of oriented gradients (PHOG) based descriptors are selected to generate shift and scale invariant descriptors of an image. PHOG-based feature descriptors are then processed in multi-layered hierarchy, following feed forward models in brain’s visual cortex and exploited through ensemble classification techniques. The proposed cortex-inspired ensemble-based object detection (CI-EnsOD) system exploits hierarchical learning mechanism of visual cortex and it is computationally efficient compared to the existing cortex-inspired models. In addition, it reduces feature dimensionality and offers improved object detection performance. The performance of proposed technique is demonstrated using three publically available standard datasets. It is shown experimentally that the prototype selection in the proposed CI-EnsOD can be improved using k-means clustering. The obtained experimental results show that the proposed CI-EnsOD technique is more accurate and efficient than contemporary cortex-inspired object detection techniques. Finally, it is also observed that the proposed technique is capable of providing compact descriptors compared to principle component analysis and independent component analysis.