Robust Face Recognition for Data Mining

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Multimedia data mining attracts considerable attention from researchers, but multimedia data mining is still at the experimental stage (Hsu, Lee & Zhang, 2002). Nowadays, the most effective way to search multimedia archives is to search the metadata of the archive, which are normally labeled manually by humans. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them — and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone.

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Robust Face Recognition for Data Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch226 ◽

2008 ◽

pp. 3621-3629

Author(s):

Brian C. Lovell ◽

Shaokang Chen

Keyword(s):

Data Mining ◽

Interactive Television ◽

Multimedia Data ◽

The Internet ◽

Data Types ◽

Text Documents ◽

Text Data ◽

Internet Backbone ◽

Large Databases ◽

Robust Face Recognition

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the Internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata, such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them — and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, post-production of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the Internet backbone.

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Robust Face Recognition for Data Mining

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch182 ◽

2011 ◽

pp. 965-972

Author(s):

Brain C. Lovell ◽

Shaokang Chen

Keyword(s):

Data Mining ◽

Interactive Television ◽

Multimedia Data ◽

The Internet ◽

Data Types ◽

Text Documents ◽

Text Data ◽

Internet Backbone ◽

Large Databases ◽

Robust Face Recognition

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the Internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata, such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them — and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, post-production of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the Internet backbone.

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Robust Face Recognition for Data Mining

Information Security and Ethics ◽

10.4018/978-1-59904-937-3.ch080 ◽

2008 ◽

pp. 1165-1175

Author(s):

Brian C. Lovell ◽

Shaokang Chen

Keyword(s):

Data Mining ◽

Interactive Television ◽

Multimedia Data ◽

The Internet ◽

Data Types ◽

Text Documents ◽

Text Data ◽

Internet Backbone ◽

Large Databases ◽

Robust Face Recognition

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the Internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata, such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them — and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, post-production of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the Internet backbone.

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Audio and Speech Processing for Data Mining

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch017 ◽

2011 ◽

pp. 98-103 ◽

Cited By ~ 1

Author(s):

Zheng-Hua Tan

Keyword(s):

Data Mining ◽

Speech Processing ◽

Large Data ◽

Data Preprocessing ◽

Multimedia Data ◽

Data Sets ◽

Data Types ◽

Multimedia Data Mining ◽

Customer Preferences ◽

And Storage

The explosive increase in computing power, network bandwidth and storage capacity has largely facilitated the production, transmission and storage of multimedia data. Compared to alpha-numeric database, non-text media such as audio, image and video are different in that they are unstructured by nature, and although containing rich information, they are not quite as expressive from the viewpoint of a contemporary computer. As a consequence, an overwhelming amount of data is created and then left unstructured and inaccessible, boosting the desire for efficient content management of these data. This has become a driving force of multimedia research and development, and has lead to a new field termed multimedia data mining. While text mining is relatively mature, mining information from non-text media is still in its infancy, but holds much promise for the future. In general, data mining the process of applying analytical approaches to large data sets to discover implicit, previously unknown, and potentially useful information. This process often involves three steps: data preprocessing, data mining and postprocessing (Tan, Steinbach, & Kumar, 2005). The first step is to transform the raw data into a more suitable format for subsequent data mining. The second step conducts the actual mining while the last one is implemented to validate and interpret the mining results. Data preprocessing is a broad area and is the part in data mining where essential techniques are highly dependent on data types. Different from textual data, which is typically based on a written language, image, video and some audio are inherently non-linguistic. Speech as a spoken language lies in between and often provides valuable information about the subjects, topics and concepts of multimedia content (Lee & Chen, 2005). The language nature of speech makes information extraction from speech less complicated yet more precise and accurate than from image and video. This fact motivates content based speech analysis for multimedia data mining and retrieval where audio and speech processing is a key, enabling technology (Ohtsuki, Bessho, Matsuo, Matsunaga, & Kayashi, 2006). Progress in this area can impact numerous business and government applications (Gilbert, Moore, & Zweig, 2005). Examples are discovering patterns and generating alarms for intelligence organizations as well as for call centers, analyzing customer preferences, and searching through vast audio warehouses.

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