15,917 research outputs found
Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation
A cloud server spent a lot of time, energy and money to train a Viola-Jones
type object detector with high accuracy. Clients can upload their photos to the
cloud server to find objects. However, the client does not want the leakage of
the content of his/her photos. In the meanwhile, the cloud server is also
reluctant to leak any parameters of the trained object detectors. 10 years ago,
Avidan & Butman introduced Blind Vision, which is a method for securely
evaluating a Viola-Jones type object detector. Blind Vision uses standard
cryptographic tools and is painfully slow to compute, taking a couple of hours
to scan a single image. The purpose of this work is to explore an efficient
method that can speed up the process. We propose the Random Base Image (RBI)
Representation. The original image is divided into random base images. Only the
base images are submitted randomly to the cloud server. Thus, the content of
the image can not be leaked. In the meanwhile, a random vector and the secure
Millionaire protocol are leveraged to protect the parameters of the trained
object detector. The RBI makes the integral-image enable again for the great
acceleration. The experimental results reveal that our method can retain the
detection accuracy of that of the plain vision algorithm and is significantly
faster than the traditional blind vision, with only a very low probability of
the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE
International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul
14, 2017, Hong Kong, Hong Kon
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
Aesthetic quality prediction is a challenging task in the computer vision
community because of the complex interplay with semantic contents and
photographic technologies. Recent studies on the powerful deep learning based
aesthetic quality assessment usually use a binary high-low label or a numerical
score to represent the aesthetic quality. However the scalar representation
cannot describe well the underlying varieties of the human perception of
aesthetics. In this work, we propose to predict the aesthetic score
distribution (i.e., a score distribution vector of the ordinal basic human
ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs
which aim to minimize the difference between the predicted scalar numbers or
vectors and the ground truth cannot be directly used for the ordinal basic
rating distribution. Thus, a novel CNN based on the Cumulative distribution
with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic
score distribution of human ratings, with a new reliability-sensitive learning
method based on the kurtosis of the score distribution, which eliminates the
requirement of the original full data of human ratings (without normalization).
Experimental results on large scale aesthetic dataset demonstrate the
effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans,
Louisiana, USA. 2-7 Feb. 201
Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach
A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for PV power generation is constructed based on extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.Department of Electrical Engineerin
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