Multimedia Data Mining and Analytics
Description
Reviews / Votes
"Multimedia data mining and analytics: disruptive innovation highlights new applications in multimedia data mining, presenting fascinating techniques together with comprehensive cases in practice. . this book is valuable for the insight it provides related to the challenges faced by fast developing technologies, their current needs and future promise. It is a practical guide, a useful handbook for academies and industry practitioners who have interest in multimedia data analysis." (Shanshan Qi, Information Technology & Tourism, Vol. 16, 2016)More details
Other editions
Additional editions
Persons
Aaron K. Baughman is a member of the Special Events Group at IBM (USA) for World Wide Sports. Previously, he was Technical Lead on a DeepQA Embed Research project that included an instance of the Jeopardy! Challenge.
Jiang (John) Gao is a Principal Scientist in the Advanced Development and Technology Group at Nokia USA, working on multimedia and mobile applications, data mining and computer vision.
Jia-Yu Pan is a software engineer at Google (USA), working on data mining and anomaly detection in big data.
Valery A. Petrushin is a Principal Scientist in the Research and Development Group at Opera Solutions (USA). His previous publications include the successful Springer title Multimedia Data Mining and Knowledge Discovery .
Content
Part I: Introduction.- Disruptive Innovation: Large Scale Multimedia Data Mining.- Part II: Mobile and Social Multimedia Data Exploration.- Sentiment Analysis Using Social Multimedia.- Twitter as a Personalizable Information Service.- Mining Popular Routes from Social Media.- Social Interactions over Location-Aware Multimedia Systems.- In-house Multimedia Data Mining.- Content-based Privacy for Consumer-Produced Multimedia.- Part III: Biometric Multimedia Data Processing.- Large-scale Biometric Multimedia Processing.- Detection of Demographics and Identity in Spontaneous Speech and Writing.- Part IV: Multimedia Data Modeling, Search and Evaluation.- Evaluating Web Image Context Extraction.- Content Based Image Search for Clothing Recommendations in E-Commerce.- Video Retrieval based on Uncertain Concept Detection using Dempster-Shafer Theory.- Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video.- Mining Videos for Featuresthat Drive Attention.- Exposing Image Tampering with the Same Quantization Matrix.- Part V: Algorithms for Multimedia Data Presentation, Processing and Visualization.- Fast Binary Embedding for High-Dimensional Data.- Fast Approximate K-Means via Cluster Closures.- Fast Neighborhood Graph Search using Cartesian Concatenation.- Listen to the Sound of Data.