Bird Part Localization Using Exemplar-Based Models with Enforced Pose and Subcategory Consistency

Jiongxin Liu, Peter N. Belhumeur; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2520-2527

Abstract


In this paper, we propose a novel approach for bird part localization, targeting fine-grained categories with wide variations in appearance due to different poses (including aspect and orientation) and subcategories. As it is challenging to represent such variations across a large set of diverse samples with tractable parametric models, we turn to individual exemplars. Specifically, we extend the exemplarbased models in [4] by enforcing pose and subcategory consistency at the parts. During training, we build posespecific detectors scoring part poses across subcategories, and subcategory-specific detectors scoring part appearance across poses. At the testing stage, likely exemplars are matched to the image, suggesting part locations whose pose and subcategory consistency are well-supported by the image cues. From these hypotheses, part configuration can be predicted with very high accuracy. Experimental results demonstrate significant performance gains from our method on an extensive dataset: CUB-200-2011 [30], for both localization and classification tasks.

Related Material


[pdf]
[bibtex]
@InProceedings{Liu_2013_ICCV,
author = {Liu, Jiongxin and Belhumeur, Peter N.},
title = {Bird Part Localization Using Exemplar-Based Models with Enforced Pose and Subcategory Consistency},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}