CVSep 12, 2017
A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization
arXiv:1709.03979v5102 citations
Originality Synthesis-oriented
AI Analysis
This addresses a gap in sparse coding for image processing, but appears incremental as it builds on existing concepts without claiming major breakthroughs.
The authors tackled the lack of a benchmark for measuring sparsity in image patches/groups by proposing a new approach based on rank minimization, but no concrete results or numbers are provided in the abstract.
Sparse coding has achieved a great success in various image processing tasks. However, a benchmark to measure the sparsity of image patch/group is missing since sparse coding is essentially an NP-hard problem. This work attempts to fill the gap from the perspective of rank minimization. More details please see the manuscript....