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As the low maintenance, cloud computing supply an capable solution for sharing group resource within cloud users. Sharing data with the two or more owners while preserving data and identity privacy from an un-trusted cloud is still an issue, due to the frequent change of the membership. In this paper, we propose a secure data sharing scheme for dynamic subgroups in the cloud. By using group signature and dynamic broadcast encryption techniques, only privileged cloud user can store and share data. Meanwhile, the storage overhead and encryption computation cost of our scheme are independent with the number of revoked users.

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.

This paper proposes an Image Retrieval model using Multiple Feature Sets and Artificial Neural Network (IR-MFS-ANN), where the multiple features Histogram of oriented Gradient (HoG), Overlapping Local Binary Pattern (OLBP), Color and Statistical features are considered. Visual information is one of the most important data in the field of social networking, medicine, military, and these areas contain an enormous volume of semi-organized and organized heterogeneous information related to explicit subjects. However, retrieval and usage of suitable information from the comprehensive information archives are important to meet the content extraction and retrieval challenges. To improve retrieval performance, the image representation, modeling, scalable algorithm that permit accessing large archives are integrated into the retrieval framework. The proposed model utilizes ANN to find the distance between feature vectors. The proposed algorithm is tested and analyzed with various retrieval techniques and it is found that ANN-based image retrieval outperforms the state-of-the-art techniques [1–5]. The proposed method results in accuracy of 94%, 92%, 95%, and 94% for Wang, Cifar-10, Oxford Flower, and ImageNet standard databases respectively.