IEEE 2017-2018 Multimedia Projects in Java

Abstract:

Precise friend recommendation is an important problem in social media. Although most social websites provide some kinds of auto friend searching functions, their accuracies are not satisfactory. In this paper, we propose a more precise auto friend recommendation method with two stages. In the first stage, by utilizing the information of the relationship between texts and users, as well as the friendship information between users, we align different social networks and choose some “possible friends.” In the second stage, with the relationship between image features and users, we build a topic model to further refine the recommendation results. Because some traditional methods, such as variational inference and Gibbs sampling, have their limitations in dealing with our problem, we develop a novel method to find out the solution of the topic model based on series expansion. We conduct experiments on the Flickr dataset to show that the proposed algorithm recommends friends more precisely and faster than traditional methods.

Abstract:

Social tags serve as a textual source of simple but useful semantic metadata that reflect user preferences or describe web objects and have been widely used in many applications. However, social tags have several unique characteristics, i.e., sparseness and data coupling (i.e. non-IIDness), which means that existing text analysis methods such as LDA are not directly applicable. In this article, we propose a new generative algorithm for social tag analysis named Joint Latent Dirichlet Allocation that models tag generation based on both users and objects and, thus, accounts for the coupling relationships among social tags. The model introduces two latent factors that jointly influence tag generation: the user's latent interest factor and the object's latent topic factor, formulated as a user-topic distribution matrix and an object-topic distribution matrix, respectively. A Gibbs sampling approach is adopted to simultaneously infer the above two matrices as well as a topic-word distribution matrix. Experimental results on four social tagging datasets showed that our model is able to capture more reasonable topics and achieves better performance than five state-of-the-art topic models in terms of the widely-used point-wise mutual information (PMI) metric. In addition, we analyze the learned topics and show that our model recovers more themes from social tags, while LDA may lead to vanishing topic problems. We also demonstrate our model's advantages in social recommendation by evaluating the retrieval results using the Mean Reciprocal Rank (MRR) metric. Finally, we explore the joint procedure of our model in depth to show the non-IID characteristics of the social tagging process.

Abstract:

We consider an interactive multiview video streaming (IMVS) system where clients select their preferred viewpoint in a given navigation window. To provide high quality IMVS, many high quality views should be transmitted to the clients. However, this is not always possible due to the limited and heterogeneous capabilities of the clients. In this paper, we propose a novel adaptive IMVS solution based on a layered multiview representation where camera views are organized into layered subsets to match the different clients constraints. We formulate an optimization problem for the joint selection of the views subsets and their encoding rates. Then, we propose an optimal and a reduced computational complexity greedy algorithms, both based on dynamic-programming. Simulation results show the good performance of our novel algorithms compared to a baseline algorithm, proving that an effective IMVS adaptive solution should consider the scene content and the client capabilities and their preferences in navigation.