Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Combining supervised learning with color correlograms for content-based image retrieval
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
A flexible image database system for content-based retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Data Structures for Range Searching
ACM Computing Surveys (CSUR)
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Intelligent Indexing and Semantic Retrieval of Multimodal Documents
Information Retrieval
AMORE: A World Wide Web image retrieval engine
World Wide Web
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
ImageRover: A Content-Based Image Browser for the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Metric-Based Shape Retrieval in Large Databases
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
CBIR using Perception based Texture and Colour Measures
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
PicToSeek: combining color and shape invariant features for image retrieval
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Semantic image classification using statistical local spatial relations model
Multimedia Tools and Applications
Kernel Based Approach for High Dimensional Heterogeneous Image Features Management in CBIR Context
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Speed up interactive image retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
iScope: personalized multi-modality image search for mobile devices
Proceedings of the 7th international conference on Mobile systems, applications, and services
Ego-similarity measurement for relevance feedback
Expert Systems with Applications: An International Journal
Local-feature-based image retrieval with weighted relevance feedback
International Journal of Business Intelligence and Data Mining
Personalized multi-modality image management and search for mobile devices
Personal and Ubiquitous Computing
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In content based image retrieval (CBIR) system, search engine retrieves the images similar to the query image according to a similarity measure. It should be fast enough and must have a high precision of retrieval. Indexing scheme is used to achieve a fast response and relevance feedback helps in improving the retrieval precision. In this paper, a human perception based similarity measure is presented and based on it a simple yet novel indexing scheme with relevance feedback is discussed. The indexing scheme is designed based on the primary and secondary keys which are selected by analysing the entropy of features. A relevance feedback method is proposed based on Mann-Whitney test. The test is used to identify the discriminating features from the relevant and irrelevant images in a retrieved set. Then emphasis of the discriminating features are updated to improve the retrieval performance. The relevance feedback scheme is implemented for two different similarity measure (Euclidean distance based and human perception based). The experiment justifies the effectiveness of the proposed methodologies. Finally, the indexing scheme and relevance feedback mechanism are combined to build up the search engine.