A note on the iterative object symmetry transform
Pattern Recognition Letters
A planar-reflective symmetry transform for 3D shapes
ACM SIGGRAPH 2006 Papers
The S-kernel: A measure of symmetry of objects
Pattern Recognition
Iterative symmetry detection: Shrinking vs. decimating patterns
Integrated Computer-Aided Engineering
S_Kernel: a new symmetry measure
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
The s-kernel and a symmetry measure based on correlation
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Automatically detecting symmetries in decorative tiles
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Self-similarity and points of interest in textured images
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
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The goal of image retrieval is to retrieve images OsimilarO to a given query image by comparing the query and database using visual attributes like color, texture and appearance. In this paper, we discuss how to characterize appearance and use it for image retrieval. Visual appearance is represented by the outputs of a set of Gaussian derivative filters applied to an image. These outputs are computed off-line and stored in a database. A query is created by outlining portions of the query image deemed useful for retrieval by the user (this may be changed interactively depending on e results). The query is also filtered with Gaussian derivatives and these outputs are compared with those from the database. The images in