929 research outputs found
Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions
Image retargeting aims to alter the size of the image with attention to the
contents. One of the main obstacles to training deep learning models for image
retargeting is the need for a vast labeled dataset. Labeled datasets are
unavailable for training deep learning models in the image retargeting tasks.
As a result, we present a new supervised approach for training deep learning
models. We use the original images as ground truth and create inputs for the
model by resizing and cropping the original images. A second challenge is
generating different image sizes in inference time. However, regular
convolutional neural networks cannot generate images of different sizes than
the input image. To address this issue, we introduced a new method for
supervised learning. In our approach, a mask is generated to show the desired
size and location of the object. Then the mask and the input image are fed to
the network. Comparing image retargeting methods and our proposed method
demonstrates the model's ability to produce high-quality retargeted images.
Afterward, we compute the image quality assessment score for each output image
based on different techniques and illustrate the effectiveness of our approach.Comment: 18 pages, 5 figure
Self-Play Reinforcement Learning for Fast Image Retargeting
In this study, we address image retargeting, which is a task that adjusts
input images to arbitrary sizes. In one of the best-performing methods called
MULTIOP, multiple retargeting operators were combined and retargeted images at
each stage were generated to find the optimal sequence of operators that
minimized the distance between original and retargeted images. The limitation
of this method is in its tremendous processing time, which severely prohibits
its practical use. Therefore, the purpose of this study is to find the optimal
combination of operators within a reasonable processing time; we propose a
method of predicting the optimal operator for each step using a reinforcement
learning agent. The technical contributions of this study are as follows.
Firstly, we propose a reward based on self-play, which will be insensitive to
the large variance in the content-dependent distance measured in MULTIOP.
Secondly, we propose to dynamically change the loss weight for each action to
prevent the algorithm from falling into a local optimum and from choosing only
the most frequently used operator in its training. Our experiments showed that
we achieved multi-operator image retargeting with less processing time by three
orders of magnitude and the same quality as the original multi-operator-based
method, which was the best-performing algorithm in retargeting tasks.Comment: Accepted to ACM Multimedia 202
Objective quality prediction of image retargeting algorithms
Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of salient regions; ii) analysis of the influence of artifacts; iii) preservation of the global structure of the image; iv) compliance with well-established aesthetics rules; and v) preservation of symmetry. Experiments on the RetargetMe benchmark, as well as a comprehensive additional user study, demonstrate that our proposed objective quality assessment method outperforms other existing metrics, while correlating better with human judgements. This makes our metric a good predictor of subjective preference
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