Abstract
Object Recognition in a large scale collection of images has become an important application of widespread use. In this setting, the goal is to find the matching image in the collection given a probe image containing the same object. In this work we explore the different possible parameters of the bag of words (BoW) approach in terms of their recognition performance and computational cost. We make the following contributions: 1) we provide a comprehensive benchmark of the two leading methods for BoW: inverted file and min-hash; and 2) we explore the effect of the different parameters on their recognition performance and run time, using four diverse real world datasets.
| Original language | English (US) |
|---|---|
| Title of host publication | VISAPP 2011 - Proceedings of the International Conference on Computer Vision Theory and Application |
| Pages | 299-306 |
| Number of pages | 8 |
| State | Published - 2011 |
| Externally published | Yes |
| Event | International Conference on Computer Vision Theory and Application, VISAPP 2011 - Vilamoura, Algarve, Portugal Duration: Mar 5 2011 → Mar 7 2011 |
Other
| Other | International Conference on Computer Vision Theory and Application, VISAPP 2011 |
|---|---|
| Country/Territory | Portugal |
| City | Vilamoura, Algarve |
| Period | 03/5/11 → 03/7/11 |
Keywords
- Bag of words
- Benchmark
- Image retrieval
- Image search
- Inverted file
- Min hash
- Object recognition
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition