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Robust object recognition based on HMAX model architecture
Chang, Yongxin1,2,3; Xu, Zhiyong1; Zhang, Jing2; Fu, Chengyu1; Gao, Chunming2; Chang, Y. (cyongxin@126.com)
Volume8558
Pages85581P
2012
Language英语
ISSN0277786X
DOI10.1117/12.999350
Indexed ByEi
Subtype会议论文
AbstractIn this paper, we describe in detail the hierarchical model and X (HMAX) model of Riesenhuber and Poggio. The HMAX model, accounting for visual processing and making plausible predictions founded on prior information, is built up by alternating simple cell layers and complex cell layers. We generalize the principal facts about the ventral visual stream and argue hierarchy of brain areas to mediate object recognition in visual cortex. Then, in order to obtain the futures of object, we implement Gabor filters and alternately apply template matching and maximum operations for input image. Finally, according to the target feature saliency and position information, we introduce a novel algorithm for object recognition in clutter based on the HMAX architecture. The improved model is competitive with current recognizing algorithms on standard database, such as the UICI car and the Caltech101 database including a large number of diverse categories. We also prove that the approach combining spatial position information of parts with the feature fusing can further promotes the recognition rate. The experimental results demonstrate that the proposed approach can recognize objects more precisely and the performance outperforms the standard model. © Copyright SPIE.; In this paper, we describe in detail the hierarchical model and X (HMAX) model of Riesenhuber and Poggio. The HMAX model, accounting for visual processing and making plausible predictions founded on prior information, is built up by alternating simple cell layers and complex cell layers. We generalize the principal facts about the ventral visual stream and argue hierarchy of brain areas to mediate object recognition in visual cortex. Then, in order to obtain the futures of object, we implement Gabor filters and alternately apply template matching and maximum operations for input image. Finally, according to the target feature saliency and position information, we introduce a novel algorithm for object recognition in clutter based on the HMAX architecture. The improved model is competitive with current recognizing algorithms on standard database, such as the UICI car and the Caltech101 database including a large number of diverse categories. We also prove that the approach combining spatial position information of parts with the feature fusing can further promotes the recognition rate. The experimental results demonstrate that the proposed approach can recognize objects more precisely and the performance outperforms the standard model. © Copyright SPIE.
Conference NameProceedings of SPIE: Optoelectronic Imaging and Multimedia Technology II
Conference Date2012
Citation statistics
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/7694
Collection光电探测与信号处理研究室(五室)
Corresponding AuthorChang, Y. (cyongxin@126.com)
Affiliation1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
2. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China
3. Graduate University, Chinese Academy of Sciences, Beijing 100039, China
Recommended Citation
GB/T 7714
Chang, Yongxin,Xu, Zhiyong,Zhang, Jing,et al. Robust object recognition based on HMAX model architecture[C],2012:85581P.
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