6,195 research outputs found
An oil painters recognition method based on cluster multiple kernel learning algorithm
A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly
Differentially Private Mixture of Generative Neural Networks
Generative models are used in a wide range of applications building on large
amounts of contextually rich information. Due to possible privacy violations of
the individuals whose data is used to train these models, however, publishing
or sharing generative models is not always viable. In this paper, we present a
novel technique for privately releasing generative models and entire
high-dimensional datasets produced by these models. We model the generator
distribution of the training data with a mixture of generative neural
networks. These are trained together and collectively learn the generator
distribution of a dataset. Data is divided into clusters, using a novel
differentially private kernel -means, then each cluster is given to separate
generative neural networks, such as Restricted Boltzmann Machines or
Variational Autoencoders, which are trained only on their own cluster using
differentially private gradient descent. We evaluate our approach using the
MNIST dataset, as well as call detail records and transit datasets, showing
that it produces realistic synthetic samples, which can also be used to
accurately compute arbitrary number of counting queries.Comment: A shorter version of this paper appeared at the 17th IEEE
International Conference on Data Mining (ICDM 2017). This is the full
version, published in IEEE Transactions on Knowledge and Data Engineering
(TKDE
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