Guest Editorial: Big Media Data: Understanding, Search, and Mining
BIG media data is a new research area, and has been attracting a lot of research interests in both industry and academia. This editorial, as the fourth part of this special issue, introduces four papers on fine-grained image classification, object retrieval, and applications of social media. The first paper is “Learning to Classify Fine-GrainedCategories with Privileged Visual-Semantic Misalignment” by K. Chen and Z. Zhang. The authors introduce a novel concept of visual-semantic misalignment in a multiclass costsensitive classification framework for classifying finegrained object classes. The proposed framework is capable ofmitigating feature inconsistency and discovering label correlation in the semantic label embedding space. Experiment results demonstrate the effectiveness of the proposed framework on public fine-grained benchmarks with achieving superior performance to state-of-the-arts. In their paper “Retrieving Objects by Partitioning,” Z. Chen, W. Zhang, B. Hu, X. Cao, S. Liu, and D. Meng present an object retrieval approach based on image partitioning to address the background-interference problem. In specific, several object proposals with maximum objectness score and minimum redundancy for each image are discovered, and then the objects are retrieved based on the partitioned proposals, separately and independently to the background. Combined with feature encoding and object level connectivity, the proposed approach achieves promising performance over three object retrieval benchmark datasets (Oxford Building, Paris, Holiday). The third paper is “Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Distinctive Metropolitan Areas” by T. Hu, E. Bigelow, J. Luo, H. Kautz. This paper examines and compares lifestyle behaviors of people living in cities of different sizes, utilizing freely available social media data, which serves as a large-scale, low-cost alternative to traditional survey methods. Two cities in the study are used: The Greater New York City area as a representative for large cities, and the Greater Rochester area as a representative for smaller cities in the United States. Technically, the authors employed matrix factor analysis as an unsupervised method to extract salient mobility and work-rest patterns for a large population of users within each metropolitan area. Interesting human behavior patterns at both a large scale and a fine granularity are discovered. It is believed that the proposed social media-based approach to lifestyle analysis represents a powerful tool for social computing in the big data age. G. Zhao, X. Qian, and C. Kang present, in their paper “Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations,” a service rating prediction approach by using the mobile users’ location sensitive characteristics. The authors mine two connections: user-item geographical connection and user-user geographical connection, and combine three factors: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity, leading to a unified rating prediction model. Experiments on a real social rating network dataset Yelp demonstrate that the proposed approach achieves superior performance to existing models. We would like to extend our thanks to the authors and the reviewers for their efforts. We also thank the staff of TBD and especially Kathy Santa Maria for their help and guidance throughout the publication process.