Artificial intelligence
International Journal of Computer Vision
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Design and evaluation of algorithms for image retrieval by spatial similarity
ACM Transactions on Information Systems (TOIS)
Multimedia Systems - Special issue on content-based retrieval
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Viewpoint-Invariant Indexing for Content-Based Image Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
On computing global similarity in images
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Least squares quantization in PCM
IEEE Transactions on Information Theory
Low-dimensional and comprehensive color texture description
Computer Vision and Image Understanding
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This paper proposes a new method for textured image retrieval, by the modal analysis of quantized spectral point patterns as the modal correspondence method of Shapiro and Brady, to match point sets by comparing the eigenvectors of a pairwise point proximity matrix taken from the power spectrum peaks. A variant of the Carcassoni, Ribeiro and Hancock method for performing recognition is taken into account. For choosing image features to represent an image, a quantization scheme is applied. This quantization scheme acts in the spectral space given by the Fourier transform of each image. Its goal is to find a small set which represents an image efficiently, where the most important features are presented. The proposed technique is invariant to rotation and is robust in the presence of noise and damaged images. The techniques here presented are compared, and the commonly used retrieval performance measurement-precision and recall-is used as evaluation of the query results.