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<h2>Visual Sense Disambiguation</h2>


<imgstyle="float: right; width: 322px; height: 131px;" alt="visual senses" src="./old_projects_files/senses.png" hspace="10" vspace="5">

<p><strong>Polysemy</strong>
is a problem for methods that exploit image search engines to build
object category models. Previously, unsupervised approaches did not
take word sense into consideration. We propose a new method that uses a
dictionary to learn models of visual word sense from a large collection
of unlabeled web data. The use of LDA to discover a latent sense space
makes the model robust despite the very limited nature of dictionary
definitions. The definitions are used to learn a distribution in the
latent space that best represents a sense. The algorithm then uses the
text surrounding image links to retrieve images with high probability
of a particular dictionary sense.</p>

<p>
We also argue
that images associated with an abstract word sense
should be excluded when training a visual classifier to learn a model
of a physical
object. While image clustering can group together visually coherent
sets of returned
images, it can be difficult to distinguish whether an image cluster
relates to
a desired object or to an abstract sense of the word. We propose a
method that exploits the semantic structure of Wordnet to remove
abstract senses. Our model does not require any human supervision, and
takes as input only the name of an object category. We show results of
retrieving
concrete-sense images in two multimodal, multi-sense databases, as well
as experiment with object classifiers trained on concrete-sense images
returned by
our method for a set of ten common office objects.</p>

<p><strong>Papers:</strong></p>

K. Saenko and T. Darrell, <ahref="http://www.cs.uml.edu/~saenko/saenko_nips_2009.pdf">"Filtering Abstract Senses From Image Search Results"</a>In Proc. NIPS, December 2009, Vancouver, Canada.
<br>
K. Saenko and T. Darrell, <ahref="http://www.cs.uml.edu/~saenko/saenko_nips08.pdf">"Unsupervised Learning of Visual Sense Models for Polysemous Words"</a>. Proc. NIPS, December 2008, Vancouver, Canada. <ahref="https://drive.google.com/open?id=0B4IapRTv9pJ1SGFyeS1wYkVKNzg">Yahoo sense dataset</a>.<br>


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