From Local Similarities to Global Coding: A Framework for Coding Applications

TitleFrom Local Similarities to Global Coding: A Framework for Coding Applications
Publication TypeJournal Article
Year of Publication2015
AuthorsShaban, A., and H. R. Rabiee
JournalIEEE Transactions on Image Processing
Keywordsdiffusion kernel, image classification, image clustering., local coordinate coding, Sparse coding
AbstractFeature coding as a building block of many image processing algorithms has received great attention in recent years. In particular, the importance of the locality assumption in coding approaches has been studied in many works. We probe this assumption and claim that taking the similarity between a data point and a more global set of anchor points does not necessarily weaken the coding method as long as the underlying structure of the anchor points are taken into account. Based on this claim, we propose to capture the underlying structure by assuming a random walker over the anchor points. We show that our method is a fast approximate learning algorithm based on the diffusion map kernel. The experiments on various datasets show that by making different state-of-the-art coding algorithms aware of this structure, may boost their performance in different learning tasks.