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| Field | Value |
|---|---|
| Title: | Feature descriptor based on local intensity order relations of pixel group |
| Authors: | Liao, Wen-Hung;Wu, Chia-Chen;Lin, Ming-Ching 廖文宏 |
| Contributors: | 資訊科學系 |
| Keywords: | Image recognition; Pattern recognition; Feature descriptors; Histogram of oriented gradients; Intensity difference; Local descriptors; Order patterns; Order relation; Recognition engines; Storage requirements; Pixels |
| Date: | 2017-04 |
| Issue Date: | 2017-08-03 14:12:02 (UTC+8) |
| Abstract: | Robust image features are essential in building effective image recognition engines. These features can be constructed according to various principles, such the distribution of local gradients (Histogram of Oriented Gradients, HOG), the relationship between two pixels (Local Binary Descriptors, LBD), or local intensity order statistics (Local Intensity Order Patterns, LIOP). Because the feature dimension grows quickly as one considers the ordering relations of a group of N (N>2) pixels, few researchers have exploited local order statistics among a pixel set to define an image feature. In this paper, we propose a novel approach to construct a feature descriptor using local intensity order relations (LIOR) in a pixel group. In contrast to LIOP where the feature dimension increases drastically with the number of elements in a set, the size of LIOR is manageable. Moreover, LIOR ensures the stability of ordering by encoding the intensity differences as weights. Two different strategies for assigning the weights have been devised and tested. Experimental results indicate that the proposed methods yield better or comparable performance for different types of image degradation when compared to the original LIOP. Additionally, the storage requirement is significantly lower when the number of pixels in a group increases. © 2016 IEEE. |
| Relation: | Proceedings - International Conference on Pattern Recognition, , 1977-1981 23rd International Conference on Pattern Recognition, ICPR 2016; Cancun CenterCancun; Mexico; 4 December 2016 到 8 December 2016; 類別編號CFP16182-ART; 代碼 127420 |
| Data Type: | conference |
| DOI: | http://dx.doi.org/10.1109/ICPR.2016.7899926 |
| DCField | Value | Language |
|---|---|---|
| dc.contributor (Contributor) | 資訊科學系 | zh_Tw |
| dc.creator (Authors) | Liao, Wen-Hung;Wu, Chia-Chen;Lin, Ming-Ching | en_US |
| dc.creator (Authors) | 廖文宏 | zh_TW |
| dc.date (Date) | 2017-04 | en_US |
| dc.date.accessioned | 2017-08-03 14:12:02 (UTC+8) | - |
| dc.date.available | 2017-08-03 14:12:02 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2017-08-03 14:12:02 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/111615 | - |
| dc.description.abstract (Abstract) | Robust image features are essential in building effective image recognition engines. These features can be constructed according to various principles, such the distribution of local gradients (Histogram of Oriented Gradients, HOG), the relationship between two pixels (Local Binary Descriptors, LBD), or local intensity order statistics (Local Intensity Order Patterns, LIOP). Because the feature dimension grows quickly as one considers the ordering relations of a group of N (N>2) pixels, few researchers have exploited local order statistics among a pixel set to define an image feature. In this paper, we propose a novel approach to construct a feature descriptor using local intensity order relations (LIOR) in a pixel group. In contrast to LIOP where the feature dimension increases drastically with the number of elements in a set, the size of LIOR is manageable. Moreover, LIOR ensures the stability of ordering by encoding the intensity differences as weights. Two different strategies for assigning the weights have been devised and tested. Experimental results indicate that the proposed methods yield better or comparable performance for different types of image degradation when compared to the original LIOP. Additionally, the storage requirement is significantly lower when the number of pixels in a group increases. © 2016 IEEE. | en_US |
| dc.format.extent | 209 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (Relation) | Proceedings - International Conference on Pattern Recognition, , 1977-1981 | en_US |
| dc.relation (Relation) | 23rd International Conference on Pattern Recognition, ICPR 2016; Cancun CenterCancun; Mexico; 4 December 2016 到 8 December 2016; 類別編號CFP16182-ART; 代碼 127420 | en_US |
| dc.subject (Keywords) | Image recognition; Pattern recognition; Feature descriptors; Histogram of oriented gradients; Intensity difference; Local descriptors; Order patterns; Order relation; Recognition engines; Storage requirements; Pixels | en_US |
| dc.title (Title) | Feature descriptor based on local intensity order relations of pixel group | en_US |
| dc.type (Data Type) | conference | |
| dc.identifier.doi (DOI) | 10.1109/ICPR.2016.7899926 | |
| dc.doi.uri | http://dx.doi.org/10.1109/ICPR.2016.7899926 |
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