Dominant Orientation Patch Matching for HMAX
Lu Yanfeng(吕彦锋)1; Zhang Huazhen2; Kang Taekoo3; Lim Myotaeg2
Source PublicationNeurocomputing
AbstractThe biologically inspired model for object recognition, Hierarchical Model and X (HMAX), has attracted considerable attention in recent years. HMAX is robust (i.e., shift- and scale-invariant), but it is sensitive to rotational deformation, which greatly limits its performance in object recognition. The main reason for this is that HMAX lacks an appropriate directional module against rotational deformation, thereby often leading to mismatch. To address this issue, we propose a novel patch-matching method for HMAX called Dominant Orientation Patch Matching (DOPM), which calculates the dominant orientation of the selected patches and implements patch-to-patch matching. In contrast to patch matching with the whole target image (second layer C1) in the conventional HMAX model, which involves huge amounts of redundant information in the feature representation, the DOPM-based HMAX model (D-HMAX) quantizes the C1 layer to patch sets with better distinctiveness, then realizes patch-to-patch matching based on the dominant orientation. To show the effectiveness of D-HMAX, we apply it to object categorization and conduct experiments on the CalTech101, CalTech05, GRAZ01, and GRAZ02 databases. Our experimental results demonstrate that D-HMAX outperforms conventional HMAX and is comparable to existing architectures that have a similar framework.
KeywordObject Recognition Classification Hmax Dominant Orientation Patch Matching
Indexed BySCI
WOS IDWOS:000375506500017
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Document Type期刊论文
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Korea University
3.Sangmyung University
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GB/T 7714
Lu Yanfeng,Zhang Huazhen,Kang Taekoo,et al. Dominant Orientation Patch Matching for HMAX[J]. Neurocomputing,2016(193):155-166.
APA Lu Yanfeng,Zhang Huazhen,Kang Taekoo,&Lim Myotaeg.(2016).Dominant Orientation Patch Matching for HMAX.Neurocomputing(193),155-166.
MLA Lu Yanfeng,et al."Dominant Orientation Patch Matching for HMAX".Neurocomputing .193(2016):155-166.
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