In this paper we describe a new method for detecting and counting a repeating object in an image. While the method relies on a fairly sophisticated deformable part model, unlike existing techniques it estimates the model parameters in an unsupervised fashion thus alleviating the need for a user-annotated training data and avoiding the associated specificity. This automatic fitting process is carried out by exploiting the recurrence of small image patches associated with the repeating object and analyzing their spatial correlation. The analysis allows us to reject outlier patches, recover the visual and shape parameters of the part model, and detect the object instances efficiently. In order to achieve a practical system which is able to cope with diverse images, we describe a simple and intuitive active-learning procedure that updates the object classification by querying the user on very few carefully chosen marginal classifications. Evaluation of the new method against the state-of-the-art techniques demonstrates its ability to achieve higher accuracy through a better user experience.
|Original language||American English|
|Title of host publication||Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|State||Published - 9 Dec 2016|
|Event||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States|
Duration: 26 Jun 2016 → 1 Jul 2016
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Period||26/06/16 → 1/07/16|
Bibliographical noteFunding Information:
This work was funded by the European Research Council, ERC Starting Grant 337383 "Fast-Filtering".
© 2016 IEEE.