To realize data sovereignty, the International Data Space (IDS), adopting usage policies to determine how, when and where other enterprises or individuals may use data, has been proposed by the IDS association and widely received attention from academia and industry. However, because data in the IDS are transferred across domains, existing policy creation approaches for a single domain cannot be applied in the IDS. To address this problem, in this paper, we propose a negotiation scheme to create usage policies in the IDS. In detail, we formulate usage policy negotiation as a combinatorial auction problem and adopt the Vickrey-Clarke-Groves (VCG) mechanism to incentivize potential data providers to truthfully negotiate usage policies. Both theoretical and simulation results show that our scheme maintains truthfulness on data providers and is cost-efficient.

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant feature and improve classification accuracy. First, local correlation between features and overall correlation is calculated by mutual information. The correlation reflects the information inclusion relationship between features, so the features are evaluated and redundant features are eliminated with analyzing the correlation. Subsequently, the concept of mean impact value (MIV) is defined and the influence degree of input variables on output variables for SVM network based on MIV is calculated. The importance weights of the features described with MIV are sorted by descending order. Finally, the SVM classifier is used to implement feature selection according to the classification accuracy of feature combination which takes MIV order of feature as a reference. The simulation experiments are carried out with three standard data sets of UCI, and the results show that this method can not only effectively reduce the feature dimension and high classification accuracy, but also ensure good robustness.

With the development of smart phones, more and more mobile phone malwares have came out in the market especially on the popular platforms such as Android, which can potentially cause harm to users' information. But how to effectively detect the new malwares and malicious software variants has been a difficult problem. In view of the traditional feature extraction method based on binary program, this paper presents a method for feature extraction of JAVA source code. The method uses the Keywords Correlation Distance to compute the correlation between key codes such as API calls, Android permissions, the common parameters, and the common key words in Android malware source code. Then SVM is applied to make the system gain to accommodate the function of the new malicious software sample, so as to detect new malicious software and existing malwares. This method is different from the conventional methods which are based on the context of the text. This method combines the characteristics of the malicious software categories and operating environment to record the behavior of the malicious software. Experiments show that the method is efficient and effective in detecting malwares on Android platform.

In complex scenes, foreground saliency can hardly be detected completely, which may further result in the ambiguous cues of objects for other computer vision tasks. In this letter, an extended locality-constrained linear self-coding (eLLsC) scheme is proposed to assist to solve the saliency detection problem under the complex scenes. The locality of both spatial relation and feature distance is preserved in eLLsC, thus making the transformed code involved in the manifold ranking to prompt the generation of the saliency map with more complete foreground and clearer boundary. Experimental results on three saliency detection benchmarks demonstrate the effectiveness of the proposed hybrid method.

In this paper, a novel framework is developed to achieve effective summarization of large-scale image collection by treating the problem of automatic image summarization as the problem of dictionary learning for sparse representation, e.g., the summarization task can be treated as a dictionary learning task (i.e., the given image set can be reconstructed sparsely with this dictionary). For image set of a specific category or a mixture of multiple categories, we have built a sparsity model to reconstruct all its images by using a subset of most representative images (i.e., image summary); and we adopted the simulated annealing algorithm to learn such sparse dictionary by minimizing an explicit optimization function. By investigating their reconstruction ability under sparsity constrain and diversity constrain, we have quantitatively measure the performance of various summarization algorithms. Our experimental results have shown that our dictionary learning for sparse representation algorithm can obtain more accurate summary as compared with other baseline algorithms.

Research of graffiti character recognition and retrieval, as a branch of traditional optical character recognition (OCR), has started to gain attention in recent years. We have investigated the special challenge of the graffiti image retrieval problem and propose a series of novel techniques to overcome the challenges. The proposed bounding box framework locates the character components in the graffiti images to construct meaningful character strings and conduct image-wise and semantic-wise retrieval on the strings rather than the entire image. Using real world data provided by the law enforcement community to the Pacific Northwest National Laboratory, we show that the proposed framework outperforms the traditional image retrieval framework with better retrieval results and improved computational efficiency.

In this paper, we present a novel framework to achieve effective summarization of large-scale web images by treating the problem of automatic image summarization as the problem of dictionary learning for sparse coding, e.g., the summary of a given image set can be treated as a sparse representation of the given image set (i.e., sparse dictionary for the given image set). For a given semantic category (i.e., certain object class or image concept), we build a sparsity model to reconstruct all its relevant images by using a subset of most representative images (i.e., image summary); and a stepwise basis selection algorithm is developed to learn such sparse dictionary (i.e., image summary) by minimizing an explicit optimization function. By investigating their reconstruction ability, the reconstruction Mean Square Error (MSE) is adapted to objectively measure the performance of various algorithms for automatic image summarization. Our experimental results demonstrate that our dictionary learning for sparse representation algorithm can obtain more accurate summary as compared with other baseline algorithms for automatic image summarization.

It is well accepted that using high-dimensional multi-modal visual features for image content representation and classifier training may achieve more sufficient characterization of the diverse visual properties of the images and further result in higher discrimination power of the classifiers. However, training the classifiers in a high-dimensional multi-modal feature space requires a large number of labeled training images, which will further result in the problem of curse of dimensionality. To tackle this problem, a hierarchical feature subset selection algorithm is proposed to enable more accurate image classification, where the processes for feature selection and classifier training are seamlessly integrated in a single framework. First, a feature hierarchy (i.e., concept tree for automatic feature space partition and organization) is used to automatically partition high-dimensional heterogeneous multi-modal visual features into multiple low-dimensional homogeneous single-modal feature subsets according to their certain physical meanings and each of them is used to characterize one certain type of the diverse visual properties of the images. Second, principal component analysis (PCA) is performed on each homogeneous singlemodal feature subset to select the most representative feature dimensions and a weak classifier is learned simultaneously. After the weak classifiers and their representative feature dimensions are available for all these homogeneous single-modal feature subsets, they are combined to generate an ensemble image classifier and achieve hierarchical feature subset selection. Our experiments on a specific domain of natural images have also obtained very positive results.

In this paper, a structured max-margin learning algorithm is developed to achieve more effective training of a large number of inter-related classifiers for multilabel image annotation application. To leverage multilabel images for classifier training, each multilabel image is partitioned into a set of image instances (image regions or image patches) and an automatic instance label identification algorithm is developed to assign multiple labels (which are given at the image level) to the most relevant image instances. A K-way min-max cut algorithm is developed for automatic instance clustering and kernel weight determination, where multiple base kernels are seamlessly combined to address the issue of huge intra-concept visual diversity more effectively. Second, a visual concept network is constructed for characterizing the inter-concept visual similarity contexts more precisely in the high-dimensional multimodal feature space. The visual concept network is used to determine the inter-related learning tasks directly in the feature space rather than in the label space because feature space is the common space for classifier training and image classification. Third, a parallel computing platform is developed to achieve more effective learning of a large number of inter-related classifiers over the visual concept network. A structured max-margin learning algorithm is developed by incorporating the visual concept network, max-margin Markov networks and multitask learning to address the issue of huge inter-concept visual similarity more effectively. By leveraging the inter-concept visual similarity contexts for inter-related classifier training, our structured max-margin learning algorithm can significantly enhance the discrimination power of the inter-related classifiers. Our experiments have also obtained very positive results for a large number of object classes and image concepts.

As digital cameras and online photo sharing services become more popular, digital images are growing exponentially on the Internet. Thus supporting more effective retrieval from large-scale image collections has become a challenging issue for CBIR community. In this paper, we have developed a human-centered computing framework for assisting users to access large-scale online image collections: (a) filtering out junk images from Google images; and (b) exploring large-scale Flickr images.