24 research outputs found
A deep evaluator for image retargeting quality by geometrical and contextual interaction
An image is compressed or stretched during the multidevice displaying, which will have a very big impact on perception quality. In order to solve this problem, a variety of image retargeting methods have been proposed for the retargeting process. However, how to evaluate the results of different image retargeting is a very critical issue. In various application systems, the subjective evaluation method cannot be applied on a large scale. So we put this problem in the accurate objective-quality evaluation. Currently, most of the image retargeting quality assessment algorithms use simple regression methods as the last step to obtain the evaluation result, which are not corresponding with the perception simulation in the human vision system (HVS). In this paper, a deep quality evaluator for image retargeting based on the segmented stacked AutoEnCoder (SAE) is proposed. Through the help of regularization, the designed deep learning framework can solve the overfitting problem. The main contributions in this framework are to simulate the perception of retargeted images in HVS. Especially, it trains two separated SAE models based on geometrical shape and content matching. Then, the weighting schemes can be used to combine the obtained scores from two models. Experimental results in three well-known databases show that our method can achieve better performance than traditional methods in evaluating different image retargeting results
Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection
As online tracking continues to grow, existing anti-tracking and
fingerprinting detection techniques that require significant manual input must
be augmented. Heuristic approaches to fingerprinting detection are precise but
must be carefully curated. Supervised machine learning techniques proposed for
detecting tracking require manually generated label-sets. Seeking to overcome
these challenges, we present a semi-supervised machine learning approach for
detecting fingerprinting scripts. Our approach is based on the core insight
that fingerprinting scripts have similar patterns of API access when generating
their fingerprints, even though their access patterns may not match exactly.
Using this insight, we group scripts by their JavaScript (JS) execution traces
and apply a semi-supervised approach to detect new fingerprinting scripts. We
detail our methodology and demonstrate its ability to identify the majority of
scripts (94.9%) identified by existing heuristic techniques. We also
show that the approach expands beyond detecting known scripts by surfacing
candidate scripts that are likely to include fingerprinting. Through an
analysis of these candidate scripts we discovered fingerprinting scripts that
were missed by heuristics and for which there are no heuristics. In particular,
we identified over one hundred device-class fingerprinting scripts present on
hundreds of domains. To the best of our knowledge, this is the first time
device-class fingerprinting has been measured in the wild. These successes
illustrate the power of a sparse vector representation and semi-supervised
learning to complement and extend existing tracking detection techniques
Light field image processing: an overview
Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data