Object Part Localization Using Exemplar-based Models
By: Jiongxin Liu
Publisher: [publisher not identified]
Published: 2017
Place of publication: [New York, N.Y.?]
Language: eng
Description: ​Object part localization is a fundamental problem in computer vision, which aims to let machines understand object in an image as a configuration of parts. As the visual features at parts are usually weak and misleading, spatial models are needed to constrain the part configuration, ensuring that the estimated part locations respect both image cue and shape prior. Unlike most of the state-of-the-art techniques that employ parametric spatial models, we turn to non-parametric exemplars of part configurations. The benefit is twofold: instead of assuming any parametric yet imprecise distributions on the spatial relations of parts, exemplars literally encode such relations present in the training samples; exemplars allow us to prune the search space of part configurations with high confidence. This thesis consists of two parts: fine-grained classification and object part localization. We first verify the efficacy of parts in fine-grained classification, where we build working systems that automatically identify dog breeds, fish species, and bird species using localized parts on the object.

Then we explore multiple ways to enhance exemplar-based models, such that they can be well applied to deformable objects such as bird and human body. Specifically, we propose to enforce pose and subcategory consistency in exemplar matching, thus obtaining more reliable hypotheses of configuration. We also propose part-pair representation that features novel shape composing with multiple promising hypotheses. In the end, we adapt exemplars to hierarchical representation, and design a principled formulation to predict the part configuration based on multi-scale image cues and multi-level exemplars. These efforts consistently improve the accuracy of object part localization.
Notes: Department: Computer Science.

Thesis advisor: Peter N. Belhumeur.

Thesis (Ph.D.)--Columbia University, 2017.