  The multimodal web elements such as text and images are associated with
inherent memory costs to store and transfer over the Internet. With the limited
network connectivity in developing countries, webpage rendering gets delayed in
the presence of high-memory demanding elements such as images (relative to
text). To overcome this limitation, we propose a Canonical Correlation Analysis
(CCA) based computational approach to replace high-cost modality with an
equivalent low-cost modality. Our model learns a common subspace for low-cost
and high-cost modalities that maximizes the correlation between their visual
features. The obtained common subspace is used for determining the low-cost
(text) element of a given high-cost (image) element for the replacement. We
analyze the cost-saving performance of the proposed approach through an
eye-tracking experiment conducted on real-world webpages. Our approach reduces
the memory-cost by at least 83.35% by replacing images with text.
