Title

A Noise-Free Similarity Model For Image Retrieval Systems

Keywords

Noise reduction; Noise-free queries; Sampling-based; Semantic constraints

Abstract

Reducing noise (i.e., irrelevant regions) in image query processing is no doubt one of the key elements to achieve high retrieval effectiveness. However, existing techniques are not able to eliminate noise from similarity matching since they capture the features of the entire image area or pre-perceived objects at the database build time. In this paper, we address this outstanding issue by proposing a similarity model for noise-free queries. In our approach, users formulate their queries by specifying objects of interest, and image similarity is based only on these relevant objects. We discuss how our approach can handle translation and scaling matching as well as how space overhead can be minimized. Our experiments show that this approach, with 1/16 the storage overhead, outperforms techniques for rectangular queries and a related technique by a significant margin.

Publication Date

1-1-2001

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

4315

Number of Pages

1-11

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.410917

Socpus ID

0035052245 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0035052245

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