The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
International Journal of Computer Vision
Data mining of multidimensional remotely sensed images
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A texture thesaurus for browsing large aerial photographs
Journal of the American Society for Information Science - Special topic issue: artificial intelligence techniques for emerging information systems applications
Mining lesion-deficit associations in a brain image database
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Indexing the edges—a simple and yet efficient approach to high-dimensional indexing
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Image mining in IRIS: integrated retinal information system
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Intelligent mining in image databases, with applications to satellite imaging and to web search
Data mining and computational intelligence
Indexing Techniques for Advanced Database Systems
Indexing Techniques for Advanced Database Systems
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
An Evaluation of Color-Spatial Retrieval Techniques for Large Image Databases
Multimedia Tools and Applications
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Retrieving Similar Shapes Effectively and Efficiently
Multimedia Tools and Applications
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Classifying Objectionable Websites Based on Image Content
IDMS '98 Proceedings of the 5th International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
CASCON '98 Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research
A geometric data structure applicable to image mining and retrieval
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Development of neuro-fuzzy system for image mining
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
| Hi-index | 0.00 |
Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how lowlevel, pixel representation contained in a raw image or image sequence can be processed to identify high-level spatial objects and relationships. To meet this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful patterns/knowledge from each level.