Exemplar-based Representations for Object Detection, Association and Beyond

Abstract

Recognizing and reasoning about the objects found in an image is one of the key problems in computer vision. This thesis is based on the idea that in order to understand a novel object, it is often not enough to recognize the object category it belongs to (i.e., answering “What is this?”). We argue that a more meaningful interpretation can be obtained by linking the input object with a similar representation in memory (i.e., asking “What is this like?”). In this thesis, we present a memory-based system for recognizing and interpreting objects in images by establishing visual associations between an input image and a large database of object exemplars. These visual associations can then be used to predict properties of the novel object which cannot be deduced solely from category membership (e.g., which way is it facing? what is its segmentation? is there a person sitting on it?). Part I of this thesis is dedicated to exemplar representations and algorithms for creating visual associations. We propose Local Distance Functions and Exemplar-SVMs, which are trained separately for each exemplar and allow an instance-specific notion of visual similarity. We show that an ensemble of Exemplar-SVMs performs competitively to state-of-the-art on the PASCAL VOC object detection task. In Part II, we focus on the advantages of using exemplars over a purely category-based approach. Because Exemplar-SVMs show good alignment between detection windows and their associated exemplars, we show that it is possible to transfer any available exemplar meta-data (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding. Finally, we construct a Visual Memex, a vast graph over exemplars encoding both visual as well as spatial relationships, and apply it to an object prediction task. Our results show that exemplars provide a better notion of object context than category-based approaches.