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MINING MULTIMEDIA DOCUMENTS
Título:
MINING MULTIMEDIA DOCUMENTS
Subtítulo:
Autor:
DEY, N
Editorial:
CRC PRESS
Año de edición:
2017
ISBN:
978-1-138-03172-2
Páginas:
227
136,00 €

 

Sinopsis

Features

Introduces techniques and approaches to mining multimedia documents
Focuses on the document content: text, images, video, and sound
Provides an insight into open research problems related to multimedia document mining
Offer easy and detailed comprehension of various document contents
Also helpful for scientists and practitioners to choose appropriate approach to their problems
Summary

The information age has led to an explosion in the amount of information available to the individual and the means by which it is accessed, stored, viewed, and transferred. In particular, the growth of the internet has led to the creation of huge repositories of multimedia documents in a diverse range of scientific and professional fields, as well as the tools to extract useful knowledge from them.

Mining Multimedia Documents is a must-read for researchers, practitioners, and students working at the intersection of data mining and multimedia applications. It investigates various techniques related to mining multimedia documents based on text, image, and video features. It provides an insight into the open research problems benefitting advanced undergraduates, graduate students, researchers, scientists and practitioners in the fields of medicine, biology, production, education, government, national security and economics.



Table of Contents

Mining Multimedia Documents: An Overview. Fuzzy Decision Trees for Text Document Clustering. Towards Modeling Semi-Automatic Data Warehouses: Guided by Social Interactions. Multi-Agent System for Text Mining. The transformation of User Requirements in UML Diagrams: An Overview. An Overview of Information Extraction using Textual Case-Based Reasoning. Opinions Classification. Documents Classification Based on Text and Image Features. Content-Based Image Retrieval (CBIR). Mining Knowledge in Medical Image Databases. Segmentation for Medical Image Mining. Biological Data Mining: Techniques and Applications. Video Text Extraction and Mining. Recent Advancement in Multimedia Content using Deep Learning.