The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
WLD: A Robust Local Image Descriptor
Abstract
Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Self-citationA performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
PCA-SIFT: a more distinctive representation for local image descriptors
Ke, Y., & Sukthankar, R. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315206 · Cited by 3,237
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Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
Recent advances and trends in visual tracking: A review
Yang, H., Shao, L., Zheng, F., Wang, L., & Song, Z. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.07.024 · Cited by 607
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1125-z · Cited by 375
Self-citationLocal binary features for texture classification: Taxonomy and experimental study
Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.032 · Cited by 369
Self-citationAn Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces
Mei, S., Yang, H., & Yin, Z. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2795178 · Cited by 308
Metadata
- Authors
- Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., & Gao, W.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2010-09
- Volume
- 32
- Issue
- 9
- Pages
- 1705-1720
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2130258210
- Cited by
- 1,024
- References
- 8
- FWCI
- 32.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., & Gao, W. (2010). WLD: A Robust Local Image Descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2009.155
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