Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Invariant Feature Extraction for Image Retrieval
State-of-the-Art in Content-Based Image and Video Retrieval [Dagstuhl Seminar, 5-10 December 1999]
Invariant Features for Gray Scale Images
Mustererkennung 1995, 17. DAGM-Symposium
Fast Estimation of Invariant Features
Mustererkennung 1999, 21. DAGM-Symposium
Histogram refinement for content-based image retrieval
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Haruspex: An Image Database System for Query-by-Examples
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Proceedings of the 2011 conference on Information Modelling and Knowledge Bases XXII
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Online image retrieval system using long term relevance feedback
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
| Hi-index | 0.00 |
In this paper we present SIMBA, a content based image retrieval system performing queries based on image appearance. We consider absolute object positions irrelevant for image similarity here and therefore propose to use invariant features. Based on a general construction method (integration over the transformation group), we derive invariant feature histograms that catch different cues of image content: features that are strongly influenced by color and textural features that are robust to illumination changes. By a weighted combination of these features the user can adapt the similarity measure according to his needs, thus improving the retrieval results considerably. The feature extraction does not require any manual interaction, so that it might be used for fully automatic annotation in heavily fluctuating image databases.