Query expansion for content-based similarity search using local and global features

Document Type

Article

Publication Date

6-1-2017

Abstract

This article presents an efficient and totally unsupervised content-based similarity search method for multimedia data objects represented by high-dimensional feature vectors. The assumption is that the similarity measure is applicable to feature vectors of arbitrary length. During the offline process, different sets of features are selected by a generalized version of the Laplacian Score in an unsupervised way for individual data objects in the database. Online retrieval is performed by ranking the query object in the feature spaces of candidate objects. Those candidates for which the query object is ranked highly are selected as the query results. The ranking scheme is incorporated into an automated query expansion framework to further improve the semantic quality of the search result. Extensive experiments were conducted on several datasets to show the capability of the proposed method in boosting effectiveness without losing efficiency.

Identifier

85022320005 (Scopus)

Publication Title

ACM Transactions on Multimedia Computing Communications and Applications

External Full Text Location

https://doi.org/10.1145/3063595

e-ISSN

15516865

ISSN

15516857

Issue

3

Volume

13

Grant

DGE 1565478

Fund Ref

National Science Foundation

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