Segmentation of heterogeneous document images : an ... - Tel

The second method calculates the correlation of each feature with the label and
returns the absolute value as its weight. The third method calculates the relevance
of a feature by measuring the symmetrical uncertainty with respect to
the class label and the fourth method uses the coefficients of the normal vector
of a linear SVM as weights.
Based on the obtained weights and considering the nature of each methods,
the most important opinion comes from the weights of SVM, however there
is one feature (normalized X center) that all methods have a negative opinion
about. This feature should be removed before we move to classifier selection.
Table 3.1:
ANALYSIS OF FEATURES FOR TEXT GRAPHICS SEPARATION
tel-00912566, version 1 - 2 Dec 2013
Feature Information gain Correlation Uncertainty SVM
LogNormalDist(1/solidity) 0.024 0.086 0.076 0.131
LogNormalDist(1/elo) 0.147 0.217 0.06 0.135
LogNormalDist(height) 0.112 0.131 0.015 0.042
center.x/src.cols 0 0 0 0
center.y/src.rows 0.055 0.099 0.007 0.012
log(src.rows/height) 0.586 0.32 0.105 0.201
log(src.cols/width) 0.186 0.067 0.063 0.288
log(1/solidity) 0.231 0.126 0.076 0.315
log(1/elo) 0.152 0.324 0.067 0.099
log(HuMoment1+1) 0.28 0.196 0.178 0.711
log(HuMoment2+1) 0.158 0.394 0.214 0.727
log(HuMoment3+1) 0.094 0.279 0.11 0.323
log(HuMoment4+1) 0.108 0.286 0.112 0.134
parents 1 1 1 0.607
childs 0.014 0.096 0.013 0.075
siblings 1 0.919 0.897 0.355
3.4 Classifier selection
The goal in classifier selection is to find a classifier for our feature set that generalize
well with the data. In other words it should avoid over-fitting and perform
well for unseen data. To do so, we choose several available classifiers and train
each classifier on our dataset to find the most fitted one for our purpose.
The dataset used for this mean is again ICDAR2009 dataset for page segmentation
competition. This dataset contains 55 documents which translates to
211585 connected components of which about 6000 are graphical components.
Clearly, the dataset is biased toward text components and the amount of text
components is unnecessary large. To speed up the computation we keep all the
graphical components but we sample 12000 components from those with true
text label. To evaluate classifiers, it is necessary to use cross-validation. Here
we use a 5 folds cross-validation. In a 5 folds cross-validation the whole dataset
is divided randomly into 5 equal parts. Each time, one part is used for testing
purpose and the other four parts are used for training. This process occurs
five times and the average performance is used for the result of each classifier
evaluation.
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