Con-Text: Text Detection Using Background Connectivity for Fine-Grained Object Classification
| Authors | |
|---|---|
| Publication date | 2013 |
| Book title | MM '13 |
| Book subtitle | proceedings of the 2013 ACM Multimedia Conference : October 21-25, 2013, Barcelona, Spain |
| ISBN |
|
| Event | 2013 ACM Multimedia Conference |
| Volume | Issue number | 2 |
| Pages (from-to) | 757-760 |
| Publisher | New York: ACM |
| Organisations |
|
| Abstract | This paper focuses on fine-grained classification by detecting photographed text in images. We introduce a text detection method that does not try to detect all possible foreground text regions but instead aims to reconstruct the scene background to eliminate non-text regions. Object cues such as color, contrast, and objectiveness are used in corporation with a random forest classifier to detect background pixels in the scene. Results on two publicly available datasets ICDAR03 and a fine-grained Building subcategories of ImageNet shows the effectiveness of the proposed method. |
| Document type | Conference contribution |
| Language | English |
| Published at |
https://doi.org/10.1145/2502081.2502197
(Final published version)
|
| Other links | |
| Permalink to this page | |