Advertisement
Published in:
2006 | OriginalPaper | Chapter
Content Based Image Retrieval Based on a Nonlinear Similarity Model
Author : Guang-Ho Cha
Published in: Computational Science and Its Applications - ICCSA 2006
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
In this paper, we propose a new nonlinear paradigm to clustering, indexing and searching for content-based image retrieval (CBIR). The scheme is designed for
approximate searches
and all the work is performed in a transformed
feature space
. We first (1) map the input space into a feature space via a nonlinear map, (2) compute the top eigenvectors in that feature space, and (3) capture cluster structure based on the eigenvectors. We (4) describe each cluster with a
minimal hypersphere
containing all objects in the cluster, (5) derive the similarity measure for each cluster individually and (6) construct a
bitmap index
for each cluster. Finally we (7) model the similarity query as a
hyper-rectangular range query
and search the clusters near the query point. Our preliminary experimental results for our new framework demonstrate considerable effectiveness and efficiency in CBIR.