Multimedia Data Mining and Analytics Disruptive Innovation /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Baughman, Aaron K. (Επιμελητής έκδοσης), Gao, Jiang (Επιμελητής έκδοσης), Pan, Jia-Yu (Επιμελητής έκδοσης), Petrushin, Valery A. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2015.
• Θέματα: Computer science.; Management.; Industrial management.; Data mining.; Multimedia information systems.; Artificial intelligence.; Image processing.; Computer Science.; Multimedia Information Systems.; Data Mining and Knowledge Discovery.; Image Processing and Computer Vision.; Signal, Image and Speech Processing.; Innovation/Technology Management.; Artificial Intelligence (incl. Robotics).

This authoritative text/reference provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. Presenting a detailed exploration into the progression of the field, the book des...

Πίνακας περιεχομένων:

• 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 Features that 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.