Abstract

for supporting my research all these years. 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,

Citations