unified detection and recognition for reading text in scene images
unified detection and recognition for reading text in scene images
unified detection and recognition for reading text in scene images
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with the <strong>detection</strong> stage altogether. One important facet be<strong>in</strong>g overlooked by all<br />
of these methods is the opportunity to improve test<strong>in</strong>g per<strong>for</strong>mance by unify<strong>in</strong>g the<br />
tra<strong>in</strong><strong>in</strong>g of <strong>detection</strong> <strong>and</strong> <strong>recognition</strong> stages.<br />
Later <strong>in</strong> the thesis we will exam<strong>in</strong>e how to tie <strong>detection</strong> <strong>and</strong> <strong>recognition</strong> together<br />
dur<strong>in</strong>g learn<strong>in</strong>g as well as the operation of the system. This will rely on shar<strong>in</strong>g<br />
<strong>in</strong>termediate levels of the classifier—i.e., features. In the rema<strong>in</strong>der of this section,<br />
we review some related feature selection strategies as they apply to computer vision<br />
tasks <strong>and</strong> some recent work related to shar<strong>in</strong>g features <strong>for</strong> different tasks.<br />
1.4.1 Feature Selection<br />
Several general frameworks exist <strong>for</strong> select<strong>in</strong>g features. The two most basic are<br />
greedy <strong>for</strong>ward <strong>and</strong> backward schemes. Forward schemes <strong>in</strong>crementally add features<br />
to a model based on some criterion of feature utility. Examples of this <strong>in</strong>clude work<br />
by Viola <strong>and</strong> Jones [121], who use s<strong>in</strong>gle-feature l<strong>in</strong>ear classifiers as weak learners <strong>in</strong> a<br />
boost<strong>in</strong>g framework, add<strong>in</strong>g features with the lowest weighted error to the ensemble.<br />
A similar <strong>for</strong>ward method by Berger et al. [9] <strong>in</strong>volves add<strong>in</strong>g only those c<strong>and</strong>idates<br />
that most <strong>in</strong>crease the likelihood of a probabilistic model. Backward schemes, by<br />
contrast, selectively prune features from a model. The l 1 or Laplacian prior [128]<br />
<strong>for</strong> neural networks, maximum entropy models, logistic regression, etc. belongs to<br />
this category. In this scheme, features are effectively elim<strong>in</strong>ated from a model dur<strong>in</strong>g<br />
tra<strong>in</strong><strong>in</strong>g by fix<strong>in</strong>g their correspond<strong>in</strong>g weights to zero. Many other variants <strong>for</strong> select<strong>in</strong>g<br />
a subset of features are possible; see Blum <strong>and</strong> Langley [12] <strong>for</strong> a more thorough<br />
review.<br />
Feature types <strong>and</strong> selection strategies <strong>for</strong> visual tasks have varied widely. The<br />
Viola <strong>and</strong> Jones object detector [121] employs outputs of simple image difference<br />
features, which are similar to wavelets. There are many possible filters <strong>and</strong> only a<br />
few are discrim<strong>in</strong>ative, so a selection process is required primarily <strong>for</strong> computational<br />
efficiency. Other methods use image fragments or patches as feature descriptors.<br />
These patches may be taken directly from the image [119, 1] or an <strong>in</strong>termediate<br />
wavelet-based representation [103, 83].<br />
1.4.2 Feature Shar<strong>in</strong>g<br />
One of the most prom<strong>in</strong>ent examples of shared features <strong>in</strong> the general mach<strong>in</strong>e<br />
learn<strong>in</strong>g literature is Caruana’s work on multitask learn<strong>in</strong>g [19]. The central idea is<br />
to use a shared representation while learn<strong>in</strong>g tasks <strong>in</strong> parallel.<br />
In particular, the high-dimensional patch-based image features mentioned above<br />
are often densely sampled <strong>and</strong> vector quantized to create a discrete codebook representation.<br />
W<strong>in</strong>n et al. [130] iteratively merge code words that do not contribute to<br />
discrim<strong>in</strong>ation overall, while Jurie et al. [56] create a cluster<strong>in</strong>g algorithm designed to<br />
cover the space with code words <strong>for</strong> better discrim<strong>in</strong>ation. Bernste<strong>in</strong> <strong>and</strong> Amit [10]<br />
cluster patches <strong>in</strong> a generative model <strong>for</strong> characters. Alternatively, LeCun et al. [65]<br />
learn (rather than select <strong>for</strong>) a discrim<strong>in</strong>ative <strong>in</strong>termediate feature representation.<br />
However, all of these methods are focused on one type of <strong>recognition</strong>, either catego-<br />
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