Weiqing Min

Social event detection with robust high-order co-clustering

This paper is devoted to detecting social, real-world events from the sharing images/videos on social media sites like Flickr and YouTube. The fast growing contents make the social media sites become gold mines for social event detection, but we still need to overcome the challenge of processing the associated heterogeneous metadata, such as time-stamp, location, visual content and textual content. Different from the traditional early or late fusion with different types of metadata, we represent them into a star-structured

Mobile Landmark Search with 3D Models

K

Multimedia news digger on emerging topics from social streams

-partite graph, that is, social media itself is regarded as the central vertices set and different types of metadata are treated as the auxiliary vertices sets which are pairwise independent with each other but correlated with the central one. Based on this graph, Social Event Detection with Robust High-Order Co-Clustering (SED-RHOCC) algorithm is proposed and it includes two steps: 1) coarse event detection, 2) clusters and samples refinement. In the first step, by revealing the inter-relationship on the constructed star-structured

Cross-Platform Multi-Modal Topic Modeling for Personalized Inter-Platform Recommendation

K

Cross-Platform Emerging Topic Detection and Elaboration from Multimedia Streams

-partite graph and the intra-relationship within some metadata sets such as time-stamp, we co-cluster social media and the associated metadata separately and iteratively to avoid information loss in early/late fusion. After that, a post process is utilized to refine the clusters and social media samples in the second step. MediaEval Social Event Detection Dataset [1] and its subset are selected to demonstrate the effectiveness of our proposed approach in handling the datasets with and without non-event samples.

Joint Local and Global Consistency on Interdocument and Interword Relationships for Co-Clustering

Landmark search is crucial to improve the quality of travel experience. Smart phones make it possible to search landmarks anytime and anywhere. Most of the existing work computes image features on smart phones locally after taking a landmark image. Compared with sending original image to the remote server, sending computed features saves network bandwidth and consequently makes sending process fast. However, this scheme would be restricted by the limitations of phone battery power and computational ability. In this paper, we propose to send compressed (low resolution) images to remote server instead of computing image features locally for landmark recognition and search. To this end, a robust 3D model based method is proposed to recognize query images with corresponding landmarks. Using the proposed method, images with low resolution can be recognized accurately, even though images only contain a small part of the landmark or are taken under various conditions of lighting, zoom, occlusions and different viewpoints. In order to provide an attractive landmark search result, a 3D texture model is generated to respond to a landmark query. The proposed search approach, which opens up a new direction, starts from a 2D compressed image query input and ends with a 3D model search result.

Script-to-Movie: A Computational Framework for Story Movie Composition

With the overwhelming information from social media networks and news portals, it is crucial to provide users a complete package of visual and textual information with popular interests automatically. To this concern, we present a news detection and pushing system, called Me-Digger (Multimedia News Digger), which not only effectively detects emerging topics from social streams but also provides the corresponding information in multiple modalities. Me-digger is the first systematic effort to leverage three sources of data, that is, Twitter, Flickr and Google news, to output with vivid visual and textual contents on emerging topics. Enabled by a novel general-structured high-order co-clustering approach, it has a more accurate detection of emerging topics compared to the existing methods on micro-blog social streams.

You Are What You Eat: Exploring Rich Recipe Information for Cross-Region Food Analysis

In this paper, we investigate a novel cross- platform multimedia problem: given two platforms, Flickr and Foursquare, we conduct the recommendation between these two platforms, namely the photo recommendation from Flickr to Foursquare users and the venue recommendation from Foursquare to Flickr users. Such inter-platform recommendations enable users from one single platform to enjoy different recommendation services effectively . To solve the problem, we propose a cross- platform multi-modal topic model ( CM3TM), which is capable of: 1) differentiating between two kinds of topics, i.e., platform- specific topics only relevant to a certain platform and shared topics characterizing the knowledge shared by different platforms and 2) aligning multiple modalities from different platforms. Specifically, CM3TM can not only split the topic space into the shared topic space and platform-specific topic space and learn them simultaneously, but also enable the alignment among different modalities through the learned topic space. Given the location information, we applied the proposed CM3TM into two inter-platform recommendation applications: 1) personalized venue recommendation from Foursquare to Flickr users and 2) personalized image recommendation from Flickr to Foursquare users. We have conducted experiments on the collected large-scale real-world dataset from Flickr and Foursquare. Qualitative and quantitative evaluation results validate the effectiveness of our method and demonstrate the advantage of connecting different platforms with different modalities for the inter-platform recommendation.

Scene and viewpoint based visual summarization for landmarks

With the explosive growth of online media platforms in recent years, it becomes more and more attractive to provide users a solution of emerging topic detection and elaboration. And this posts a real challenge to both industrial and academic researchers because of the overwhelming information available in multiple modalities and with large outlier noises. This article provides a method on emerging topic detection and elaboration using multimedia streams cross different online platforms. Specifically, Twitter, New York Times and Flickr are selected for the work to represent the microblog, news portal and imaging sharing platforms. The emerging keywords of Twitter are firstly extracted using aging theory. Then, to overcome the nature of short length message in microblog, Robust Cross-Platform Multimedia Co-Clustering (RCPMM-CC) is proposed to detect emerging topics with three novelties: 1) The data from different media platforms are in multimodalities; 2) The coclustering is processed based on a pairwise correlated structure, in which the involved three media platforms are pairwise dependent; 3) The noninformative samples are automatically pruned away at the same time of coclustering. In the last step of cross-platform elaboration, we enrich each emerging topic with the samples from New York Times and Flickr by computing the implicit links between social topics and samples from selected news and Flickr image clusters, which are obtained by RCPMM-CC. Qualitative and quantitative evaluation results demonstrate the effectiveness of our method.

What happened near big ben: event-driven landmark mining from flickr

Co-clustering has recently received a lot of attention due to its effectiveness in simultaneously partitioning words and documents by exploiting the relationships between them. However, most of the existing co-clustering methods neglect or only partially reveal the interword and interdocument relationships. To fully utilize those relationships, the local and global consistencies on both word and document spaces need to be considered, respectively. Local consistency indicates that the label of a word/document can be predicted from its neighbors, while global consistency enforces a smoothness constraint on words/documents labels over the whole data manifold. In this paper, we propose a novel co-clustering method, called co-clustering via local and global consistency, to not only make use of the relationship between word and document, but also jointly explore the local and global consistency on both word and document spaces, respectively. The proposed method has the following characteristics: 1) the word-document relationships is modeled by following information-theoretic co-clustering (ITCC); 2) the local consistency on both interword and interdocument relationships is revealed by a local predictor; and 3) the global consistency on both interword and interdocument relationships is explored by a global smoothness regularization. All the fitting errors from these three-folds are finally integrated together to formulate an objective function, which is iteratively optimized by a convergence provable updating procedure. The extensive experiments on two benchmark document datasets validate the effectiveness of the proposed co-clustering method.