Recent years have seen an increasing interest in sparse representations for image classification and object recog- nition, probably motivated by evidence from the analysis of the primate visual cortex. It is still unclear, however, whether or not sparsity helps classification. In this pa- per we evaluate its impact on the recognition rate using a shallow modular architecture, adopting both standard fil- ter banks and filter banks learned in an unsupervised way. In our experiments on the CIFAR-10 and on the Caltech- 101 datasets, enforcing sparsity constraints actually does not improve recognition performance. This has an impor- tant practical impact in image descriptor design, as enforc- ing these constraints can have a heavy computational cost.
|Title of host publication||2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|
|Number of pages||8|
|Publication status||Published - Jul 2011|
|Event||IEEE Computer Vision and Pattern Recognition (CVPR) 2011 - Colorado Springs|
Duration: 21 Jun 2011 → 25 Jun 2011
|Conference||IEEE Computer Vision and Pattern Recognition (CVPR) 2011|
|Period||21/06/11 → 25/06/11|