Classifying Chart Based on Structural Dissimilarities using Improved Regularized Loss Function

摘要

Classification of charts is a major challenge because each chart class has variations due to the styles, appearances, structure, and noises caused due to changing data values. These variations differ across all chart types and sub-types. Hence, it becomes difficult for any model to learn the diversity of chart classes with the changing structures due to lack of association between the features from similar-dissimilar regions and its varied structure. In this paper, we present a novel dissimilarity based learning model for similar structured but diverse chart classification, by improving the loss function. Our approach jointly learns the features of both dissimilar and similar regions using notion of homogeneity. The loss function of the model is improved, which is fused by a variation aware dissimilarity index and incorporated with regularization parameters, making model more prone towards dissimilar regions and similar structure charts. Extensive comparative evaluations demonstrate that our approach significantly outperforms other benchmark methods, including both traditional and deep learning models, over publicly available datasets.