TRANSFER LEARNING BASED CLASSIFICATION OF SEGMENTED LANDING PAGE COMPONENTS

Year 2018, Volume: 3 Issue: 2, 36 - 39, 31.12.2018

Çağla Şenel Gülşah Ayhan Zeynep Eda Uran Behçet Uğur Töreyin

Abstract

The pages that appear in front of users on digital
platforms used for online advertising to attract attention to target product
are called landing pages. Landing pages aim to increase advertisement
conversion rate using the metrics like clicks, views or subscribes. In this
study, a method is presented to automatically classifier the most commonly used
components on landing pages which are buttons, texts, and checkboxes. Landing
page images given as inputs are segmented by morphological and threshold-based
image processing methods, and each segment is classified using a Transfer
Learning based method which combines pre-trained Inception v-3 networks and
Support Vector Classifier (SVM). Furthermore, different classifiers were
applied to compare the results. The proposed method is anticipated to be an
essential step in the process of designing landing pages automatically with
high advertisement conversion rates. Thanks to the proposed transfer learning
based method, this is achieved by using fewer number of training data.

Keywords

Landing Page Segmentation, Transfer Learning, Image Processing, Image Classification

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