International Journal of Computer Vision
Geometric invariance in computer vision
Geometric invariance in computer vision
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Content-Based Image Retrieval Based on Local Affinely Invariant Regions
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Convex Layers: A New Tool for Recognition of Projectively Deformed Point Sets
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Effective Image Retrival Using Deformable Templates
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Finding Color and Shape Patterns in Images
Finding Color and Shape Patterns in Images
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Affine invariant feature extraction using symmetry
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
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A novel approach to content-based image retrieval is presented. The method supports recognition of objects under a very wide range of viewing and illumination conditions and is robust to occlusion and background clutter. Starting from robustly detected 'distinguished regions' of data dependent shape, local affine frames are established by affine-invariant constructions exploiting invariant properties of the second moment matrix and bi-tangent points. Direct comparison of photometrically normalised colour intensities in normalised frames facilitates robust, affine and illumination invariant, but still very selective matching. The potential of the proposed approach is experimentally verified on FOCUS -- a publicly available image database - using a standard set of query images. The results obtained are superior to the state of the art. The method operates successfully on images with complex background, where the sought object covers only a fraction (around 2%) of the database image. Examples of precise localisation of the query objects in an image are shown too.