129 research outputs found
Learning Visual Importance for Graphic Designs and Data Visualizations
Knowing where people look and click on visual designs can provide clues about
how the designs are perceived, and where the most important or relevant content
lies. The most important content of a visual design can be used for effective
summarization or to facilitate retrieval from a database. We present automated
models that predict the relative importance of different elements in data
visualizations and graphic designs. Our models are neural networks trained on
human clicks and importance annotations on hundreds of designs. We collected a
new dataset of crowdsourced importance, and analyzed the predictions of our
models with respect to ground truth importance and human eye movements. We
demonstrate how such predictions of importance can be used for automatic design
retargeting and thumbnailing. User studies with hundreds of MTurk participants
validate that, with limited post-processing, our importance-driven applications
are on par with, or outperform, current state-of-the-art methods, including
natural image saliency. We also provide a demonstration of how our importance
predictions can be built into interactive design tools to offer immediate
feedback during the design process
Improvised Salient Object Detection and Manipulation
In case of salient subject recognition, computer algorithms have been heavily
relied on scanning of images from top-left to bottom-right systematically and
apply brute-force when attempting to locate objects of interest. Thus, the
process turns out to be quite time consuming. Here a novel approach and a
simple solution to the above problem is discussed. In this paper, we implement
an approach to object manipulation and detection through segmentation map,
which would help to desaturate or, in other words, wash out the background of
the image. Evaluation for the performance is carried out using the Jaccard
index against the well-known Ground-truth target box technique.Comment: 7 page
Image Cropping with Composition and Saliency Aware Aesthetic Score Map
Aesthetic image cropping is a practical but challenging task which aims at
finding the best crops with the highest aesthetic quality in an image.
Recently, many deep learning methods have been proposed to address this
problem, but they did not reveal the intrinsic mechanism of aesthetic
evaluation. In this paper, we propose an interpretable image cropping model to
unveil the mystery. For each image, we use a fully convolutional network to
produce an aesthetic score map, which is shared among all candidate crops
during crop-level aesthetic evaluation. Then, we require the aesthetic score
map to be both composition-aware and saliency-aware. In particular, the same
region is assigned with different aesthetic scores based on its relative
positions in different crops. Moreover, a visually salient region is supposed
to have more sensitive aesthetic scores so that our network can learn to place
salient objects at more proper positions. Such an aesthetic score map can be
used to localize aesthetically important regions in an image, which sheds light
on the composition rules learned by our model. We show the competitive
performance of our model in the image cropping task on several benchmark
datasets, and also demonstrate its generality in real-world applications.Comment: Accepted by AAAI 2
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