Name
Abstract | ||
|---|---|---|
Tasks such as image retrieval, scene classification, and object recognition often make use of local image features, which are intended to provide a reliable and efficient image representation. However, local feature extractors are designed to respond to a limited set of structures (e.g. blobs or corners), which might not be sufficient to capture the most relevant image content.We discuss the lack of coverage of relevant image information by local features as well as the often neglected complementarity between sets of features. As a result, we propose an information-theoretic-based keypoint extraction that responds to complementary local structures and is aware of the image composition. We empirically assess the validity of the method by analysing the completeness, complementarity, and repeatability of context-aware features on different standard datasets. Under these results, we discuss the applicability of the method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.5244/C.26.100 | PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2012 |
Field | DocType | Citations |
Kernel (linear algebra),Computer vision,Pattern recognition,Salience (neuroscience),Computer science,Image retrieval,Hessian matrix,Structure tensor,Artificial intelligence,Probability density function,Gaussian function,Kernel density estimation | Conference | 2 |
PageRank | References | Authors |
0.39 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Martins | 1 | 113 | 29.99 |
| Paulo Carvalho | 2 | 250 | 47.68 |
| C. Gatta | 3 | 521 | 37.03 |