Name
Abstract | ||
|---|---|---|
In this paper, we address the problem of video classification from a set of compressed features. In particular, the properties of linear random projections in the framework of compressive sensing are exploited to reduce the task of classifying a given video sequence into a problem of sparse reconstruction, based on feature vectors consisting of measurements lying in a low-dimensional compressed domain. This can be of great importance in decision systems with limited power, processing, and bandwidth resources, since the classification is performed without handling the original high-resolution video data, but working directly with the set of compressed measurements. The experimental evaluation verifies the efficiency of the proposed scheme and illustrates that the compressed measurements in conjunction with an appropriate decision rule result in an effective video classification scheme, which meets the constraints of systems with limited resources. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/PCS.2012.6213363 | 2012 PICTURE CODING SYMPOSIUM (PCS) |
Keywords | Field | DocType |
high resolution,image reconstruction,vectors,accuracy,compressive sensing,support vector machines,feature extraction,image classification,feature vector,decision rule,sensors,data compression,compressed sensing | Decision rule,Computer vision,Pattern recognition,Computer science,Feature extraction,Video tracking,Artificial intelligence,Contextual image classification,Data compression,Video denoising,Compressed sensing,Video compression picture types | Conference |
Citations | PageRank | References |
1 | 0.38 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Tzagkarakis | 1 | 139 | 17.94 |
| Pavlos Charalampidis | 2 | 4 | 2.80 |
| Grigorios Tsagkatakis | 3 | 122 | 21.53 |
| Jean-Luc Starck | 4 | 1183 | 122.27 |
| P. Tsakalides | 5 | 954 | 120.69 |