Data Mining-Based CBIR System

2016 ◽  
pp. 244-261
Author(s):  
Shruti Kohli ◽  
Vijay Shankar Gupta
Keyword(s):  
Data Mining ◽  
Social Network Sites ◽  
Image Databases ◽  
Multimedia Data ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Multimedia Networks ◽  
Useful Knowledge ◽  
Large Databases ◽  
Efficient Retrieval

Multimedia mining primarily involves information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution (STI). Content Based Image Retrieval (CBIR) is the efficient retrieval of relevant images from large databases based on features extracted from the image. The emergence and proliferation of social network sites such as Facebook, Twitter and LinkedIn and other multimedia networks such as Flickr has further accelerated the need of efficient CBIR systems. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging task. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The need of the day is New Image Mining techniques need to be explored and a self-adaptable CBIR system needs to be developed.

Data Mining-Based CBIR System

2018 ◽  
pp. 440-457
Author(s):  
Shruti Kohli ◽  
Vijay Shankar Gupta
Keyword(s):  
Data Mining ◽  
Social Network Sites ◽  
Image Databases ◽  
Multimedia Data ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Multimedia Networks ◽  
Useful Knowledge ◽  
Large Databases ◽  
Efficient Retrieval

Multimedia mining primarily involves information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution (STI). Content Based Image Retrieval (CBIR) is the efficient retrieval of relevant images from large databases based on features extracted from the image. The emergence and proliferation of social network sites such as Facebook, Twitter and LinkedIn and other multimedia networks such as Flickr has further accelerated the need of efficient CBIR systems. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging task. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The need of the day is New Image Mining techniques need to be explored and a self-adaptable CBIR system needs to be developed.

scholarly journals Robust Face Recognition for Data Mining

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.

scholarly journals Robust Face Recognition for Data Mining

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.

scholarly journals Robust Face Recognition for Data Mining

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.

Overview of Business Intelligence through Data Mining

2014 ◽  
pp. 272-295
Author(s):  
Abdulrahman R. Alazemi ◽  
Abdulaziz R. Alazemi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Information Technologies ◽  
Customer Relationships ◽  
The Internet ◽  
Competitive Advantages ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Internet Business ◽  
Future Outcomes

The advent of information technologies brought with it the availability of huge amounts of data to be utilized by enterprises. Data mining technologies are used to search vast amounts of data for vital insight regarding business. Data mining is used to acquire business intelligence and to acquire hidden knowledge in large databases or the Internet. Business intelligence can find hidden relations, predict future outcomes, and speculate and allocate resources. This uncovered knowledge helps in gaining competitive advantages, better customer relationships, and even fraud detection. In this chapter, the authors describe how data mining is used to achieve business intelligence. Furthermore, they look into some of the challenges in achieving business intelligence.

Machine Learning Approach for Content Based Image Retrieval

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Learning Approach ◽  
Query Image ◽  
Low Level ◽  
Machine Learning Approach ◽  
Efficient Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

Visual Data Mining for Discovering Association Rules

2008 ◽  
pp. 2105-2120
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Overview of Business Intelligence through Data Mining

2016 ◽  
pp. 49-72 ◽  
Author(s):  
Abdulrahman R. Alazemi ◽  
Abdulaziz R. Alazemi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Information Technologies ◽  
Customer Relationships ◽  
The Internet ◽  
Competitive Advantages ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Internet Business ◽  
Future Outcomes

The advent of information technologies brought with it the availability of huge amounts of data to be utilized by enterprises. Data mining technologies are used to search vast amounts of data for vital insight regarding business. Data mining is used to acquire business intelligence and to acquire hidden knowledge in large databases or the Internet. Business intelligence can find hidden relations, predict future outcomes, and speculate and allocate resources. This uncovered knowledge helps in gaining competitive advantages, better customer relationships, and even fraud detection. In this chapter, the authors describe how data mining is used to achieve business intelligence. Furthermore, they look into some of the challenges in achieving business intelligence.

Visual Data Mining for Discovering Association Rules

2011 ◽  
pp. 209-226
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Video Data Mining

2011 ◽  
pp. 1185-1189 ◽  
Author(s):  
Jung Hwan Oh ◽  
Jeong Kyu Lee ◽  
Sae Hwang
Keyword(s):  
Data Mining ◽  
Research Area ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Data Sets ◽  
Data Set ◽  
Useful Knowledge ◽  
Active Research ◽  
Diverse Data

Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.

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