Sparse representation and learning in visual recognition: Theory and applications
Sparse representation and learning has been widely used in computational intelligence,
machine learning, computer vision and pattern recognition, etc. Mathematically, solving …
machine learning, computer vision and pattern recognition, etc. Mathematically, solving …
Learning inter-related visual dictionary for object recognition
Object recognition is challenging especially when the objects from different categories are
visually similar to each other. In this paper, we present a novel joint dictionary learning (JDL) …
visually similar to each other. In this paper, we present a novel joint dictionary learning (JDL) …
Jointly learning visually correlated dictionaries for large-scale visual recognition applications
Learning discriminative dictionaries for image content representation plays a critical role in
visual recognition. In this paper, we present a joint dictionary learning (JDL) algorithm which …
visual recognition. In this paper, we present a joint dictionary learning (JDL) algorithm which …
A max-margin perspective on sparse representation-based classification
Abstract Sparse Representation-based Classification (SRC) is a powerful tool in
distinguishing signal categories which lie on different subspaces. Despite its wide …
distinguishing signal categories which lie on different subspaces. Despite its wide …
Bin ratio-based histogram distances and their application to image classification
Large variations in image background may cause partial matching and normalization
problems for histogram-based representations, ie, the histograms of the same category may …
problems for histogram-based representations, ie, the histograms of the same category may …
Class-discriminative kernel sparse representation-based classification using multi-objective optimization
In this paper, we propose class-discriminative kernel sparse representation-based
classification (KSRC) using multi-objective optimization (MOO) called KSRC 2.0. In sparse …
classification (KSRC) using multi-objective optimization (MOO) called KSRC 2.0. In sparse …
Set constraints for destructive array update optimization
M Wand, WD Clinger - Journal of Functional Programming, 2001 - cambridge.org
Destructive array update optimization is critical for writing scientific codes in functional
languages. We present set constraints for an interprocedural update optimization that runs in …
languages. We present set constraints for an interprocedural update optimization that runs in …
Noise reduction of diffusion tensor images by sparse representation and dictionary learning
Background The low quality of diffusion tensor image (DTI) could affect the accuracy of
oncology diagnosis. Methods We present a novel sparse representation based denoising …
oncology diagnosis. Methods We present a novel sparse representation based denoising …
基于字典学习与稀疏表示的非结构化道路分割方法
肖良， 戴斌， 吴涛， 方宇强 - 吉林大学学报(工学版), 2013 - xuebao.jlu.edu.cn
针对野外环境自主车视觉导航问题, 提出了一种新颖的基于字典学习与稀疏表示的道路分割算法
. 该算法以局部图像小片为处理单元, 通过选取典型道路图像学习得到路面图像小片的一组字典 …
. 该算法以局部图像小片为处理单元, 通过选取典型道路图像学习得到路面图像小片的一组字典 …
Player classification algorithm based on digraph in soccer video
S Sun, R Cui - 2014 IEEE 7th Joint International Information …, 2014 - ieeexplore.ieee.org
Player classification method based on the tradition histograms lacks color spatial
information, which leads to low classification performance, and, in addition, it needs priori …
information, which leads to low classification performance, and, in addition, it needs priori …