Shift Invariant Sparse Coding of Image and Music Data  Morten Mørup, Mikkel N. Schmidt, Lars K. Hansen
 Abstract  When analyzing multimedia data such as image and music it is useful to extract higherlevel features that constitute prominent signatures of the data. We demonstrate how a 2D shift invariant sparse coding model is capable of extracting such higher level features forming socalled icon alphabets for the data. For image data the model is able to find highlevel prominent features while for music the model is able to extract both the harmonic structure of instruments as well as indicate the scores they play. We further demonstrate that nonnegativity constraints are useful since they favor part based representation. The success of the model relies in finding a good value for the degree of sparsity. For this, we propose an `Lcurve'like argument and use the sparsity parameter that maximizes the curvature in the graph of the residual sum of squares plotted against the number of nonzero elements of the sparse code. Matlab implementation of the algorithm is available for download.  Keywords  sparse coding, part based representation, Lcurve, shift invariance  Type  Technical report  Year  2008  Electronic version(s)  [pdf]  BibTeX data  [bibtex]  IMM Group(s)  Intelligent Signal Processing 
Back :: IMM Publications
