Search

With the rapid growth of multimedia data (e.g., text, image, video, audio and 3D model, etc) in the web, there are a large number of media objects with different modalities in the multimedia documents such as webpages, which exhibit latent semantic correlation. As a new type of multimedia retrieval method, cross-media retrieval is becoming increasingly attractive, through which users can get the results with various media types with the same semantic information by submitting a retrieval of any media type. The explosive increasing of the number of media objects, however, makes it difficult for the traditional local standalone mode to process efficiently. So the powerful parallel processing capability of cloud computing is accommodated to facilitate the efficient large-scale cross-media retrieval. In this paper, based on a Multi-Layer-Cross-Reference-Graph(MLCRG) model, we propose an efficient parallel cross-media retrieval (PCMR) method in which two enabling techniques (i.e., 1) the adaptive cross-media data allocation algorithm and 2) the PCIndex scheme) are accommodated to effectively speedup the retrieval performance. To the best of our knowledge, there is little research on the parallel retrieval processing of the large-scale cross-media databases in the mobile cloud network. Extensive experiments are conducted to testify that our proposed PCIndex method outperform the three competitors (e.g., the PFAR (Mao et al, 22), the MBSR (Retrieval 4(2):153-164, 42) and the SPECH (Knowl Based Syst 251(5):1-13, 40)) in terms of the effectiveness and efficiency, respectively. [ABSTRACT FROM AUTHOR]

The growing popularity of social media provides a huge volume of social data including Tweets. These collections of social data can be potentially useful, but the extent of meaningful data in these collections has not been sufficiently researched, especially in South Korea Twitter data. In general, the South Korea Twitter data has been researched as a source of political media. Nonetheless, previous research on South Korea Twitter data has not adequately covered what kind of trend Twitter represents in terms of major topic categories such as politics, economics, or sports. In this paper, we present a cross-media approach to define the nature of South Korea Tweets by inferring the topic category distribution through short-text categorization. We select newspapers as cross-media, examine the categorization of news articles from major newspapers, and then train our classifier based on the features from each topic category. In addition, for grafting news topics onto South Korea Tweets, we propose a word clustering and filtering approach to exclude those words that do not provide semantic content for the topic categories. Based on the proposed procedures, we analyze the South Korea Tweets to determine the primary topic category focus of Twitter users. We observe the special behaviors of the South Korea Twitter users based on various parameters such as date, time slot, and day of the week. Because our research includes a macroscopic analysis of Twitter data using a cross-media strategy, our research can provide useful resources for other social media analysis as well. [ABSTRACT FROM AUTHOR]

Cross-media semantics understanding, which focuses on multimedia data of different modalities, is a rising hot topic in social media analysis. One of the most challenging issues for cross-media semantics understanding is how to represent multimedia data of different modalities. Most traditional multimedia semantics analysis works are based on single modality data sources, such as Flickr images or YouTube videos, leaving efficient cross-media data representation wide open. In this paper, we propose a novel nonnegative cross-media recoding approach, which learns co-occurrences of cross-media feature spaces by explicitly learning a common subset of basis vectors. Moreover, we impose the nonnegativity constraint on the decomposed matrices so that the basis vectors represent important and locally meaningful features of the cross-media data. We take two kinds of typical multimedia data, that is, image and audio, as experimental data. Our approach can be applied to a wide range of multimedia applications. The experiments are conducted on image-audio dataset for applications of cross-media retrieval and data clustering. Experiment results are encouraging and show that the performance of our approach is effective. [ABSTRACT FROM AUTHOR]

Cross-media retrieval aims to automatically perform the content-based search procedure among various media types (e.g., image, video and text), in which media representation plays an important role for providing the heterogeneous similarity measure. In this work, a novel semantic representation of cross-media, called accumulated reconstruction error vector (AREV), is proposed, which includes category-specific dictionary learning, media sample reconstruction, and accumulative reconstruction error concatenation. Instead of directly learning the correlation relationship among heterogeneous items in the same semantic groups, the AREV projects individually their original feature descriptions into a shared semantic space, in which each component is semantic consistent for various media types due to the consistency in category information. Experiments on the commonly used datasets, i.e. Wikipedia dataset and NUS-Wide dataset, show the good performance in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]