ACCV 2014 Tutorial
Mining Image and Video Data
Junsong Yuan, Nanyang Technological University, Singapore, firstname.lastname@example.org
Ying Wu, Northwestern University, USA
Time: 9AMó1230PM, Nov. 01, 2014
Location: Global Learning Room,† University Town/Stephen Riady Centre, NUS, Singapore
Motivated by the previous success in mining structured data (e.g., transaction data) and semi-structured data (e.g., text), it has aroused our curiosity in mining meaningful patterns in non-structured multimedia data like images and videos. Although the discovery of visual patterns from images and videos appears to be quite exciting, data mining techniques that are successful in business and text data may not be simply applied to image and video data that contain high-dimensional features and have spatial or spatio-temporal structures. Unlike transaction and text data that are composed of discrete elements without much ambiguity (i.e. predefined items and vocabularies), visual patterns generally exhibit large variabilities in their visual appearances, thus challenge existing data mining and pattern discovery algorithms. This tutorial will discuss the state of the art of image and video data mining, and provide in-depth studies on some of the recently developed techniques. The topics cover bottom-up common visual pattern discovery, top-down visual pattern discovery using topic model, abnormal video pattern discovery, as well as their applications in image search and recognition, scene understanding, video summarization and anomaly detection, intelligent video surveillance, etc.