Computer vision core: Image and video descriptors

Core part of research in CMVS is fundamental research on generic algorithms for computer vision. The Center has achieved ground-breaking research results in many areas of its activity, including texture and dynamic texture analysis.

Image and video descriptors play a key role in most computer vision systems and applications. The function of descriptors is to convert pixel-level  information into a useful form, which captures the most important factors  into a useful form but is insensitive  to irrelevant aspects caused by the varying environment.  While the definition of irrelevant depends on the application, the most common cases are related to imaging conditions like illumination, viewing angle, scale, noise, and blur. The main focus of our research  has been  in  texture  descriptors, in  which area we have long traditions and  rank among the world leaders.  

Texture is an important characteristic of many types of images and  videos. The Local Binary Pattern (LBP) texture operator has been highly successful in numerous applications around the world, and has inspired plenty of new research on related methods, including the blur-insensitive Local Phase Quantization (LPQ) method, Weber Law Descriptor (WLD), Binarized Statistical Image Features (BSIF), and  Local Orientation Adaptive Descriptor (LOAD) also developed by researchers of CMVS. Spatio-temporal dynamic  texture descriptors for video analysis have also been proposed. The most popular one among these is LBP-TOP.  One focus of current research is in  combining these kind of “traditional” descriptors with deep learning based approaches, with applications  in face analysis and biomedical image analysis, for example.

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Selected References

Ojala T, Pietikäinen M & Mäenpää T (2002)  Multiresolution  gray-scale and rotation invariant texture classification with Local Binary Patterns. IEEE Transactions on  Pattern Analysis and Machine Intelligence 24(7):971-987. 

Ojansivu V & Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. Proc. Image and Signal Processing (ICISP 2008), Cherbourg-Octeville, France, 5099:236-243.

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 32(9):1705-1720.

Kannala J & Rahtu E (2012) BSIF: Binarized statistical image features. Proc. 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 1363-1366.

Qi X, Zhao G, Shen L, Li Q & Pietikäinen M (2016) LOAD: Local orientation adaptive descriptor for texture and material classification. Neurocomputing 184:28-35.

Zhao G & Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6):915-928.

Liu L, Fieguth P, Wang X, Pietikäinen M & Hu D (2016)  Evaluation of LBP and deep texture descriptors with a new robustness benchmark. Proc. European Conference on Computer Vision, in press.

Last updated: 22.8.2016