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Spectral Clustering and Dimensionality Reduction Applied to Content Based Image Retrieval with Hybrid Descriptors

Abstract : The topic of research exposed in this paper concerns Content Based image retrieval in a heterogeneous high database. The increase of storage capacities and the evolution of compression techniques have generated an explosion of the digital information quantity. Their computerization opens a vast field of applications. In this setting we are interested more especially in the problem of the dimensionality reduction and spectral clustering of a heterogeneous database of images in order to image retrieval by the content. Our new gait described in this paper consists to: in first phase the description of the database images by a hybrid descriptor which are Interest SIFT points combined with texture descriptor given by the application of Wavelet transform. The descriptor is multi-dimensional, robust and invariant to changes and scales. In second phase the representation of the database images as a convex graph. In third phase the reduction of the space of representation by the application of an unsupervised spectral classification (The Spectral training uses information contained in the eigenvectors of the normalized matrix of transition to detect structures in data.) That will provide us classes of images that has shortcoming the Eigen-values calculated on the matrix of symmetry. As last phase, we use the Nyström theory that will permit us, not to recalculate the all Eigen-values, but only the lasts one.
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Submitted on : Thursday, April 18, 2013 - 2:07:37 PM
Last modification on : Saturday, June 25, 2022 - 9:47:20 AM


  • HAL Id : hal-00815253, version 1



Kamel Houari, Youssef Chahir, Mohamed-Khireddine Kholladi. Spectral Clustering and Dimensionality Reduction Applied to Content Based Image Retrieval with Hybrid Descriptors. International Review on Computers and Software (IRECOS), Praise Worthy Prize, 2009, 4 (6), pp.633-639. ⟨hal-00815253⟩



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