NDLI: Reducing the dimensionality of the SIFT descriptor and increasing its effectiveness and efficiency in image retrieval via bag-of-features

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Reducing the dimensionality of the SIFT descriptor and increasing its effectiveness and efficiency in image retrieval via bag-of-features

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Reducing the dimensionality of the SIFT descriptor and increasing its effectiveness and efficiency in image retrieval via bag-of-features

Content Provider	ACM Digital Library
Author	Rezende, Solange Oliveira Pedrosa, Glauco Vitor Traina, Agma Juci Machado
Abstract	The Bag-of-Features is a popular approach to describe multimedia information by using visual words. The SIFT (Scale Invariant Feature Transform) is one of the most utilized descriptor to model multimedia information in Bag of-Features. The data is described as a set of keypoints and a feature vector is assigned for each of the keypoints. This feature vector is composed of 128 values, which represent the region around each keypoint. In general, some of the detected keypoints are not relevant and can be discarded without losing the local discriminative power. In this paper, we propose a technique to reduce the detected keypoints by SIFT, as well as a technique to reduce the feature vector dimensionality. Experiments were made in order to analyze the performance of the proposed reduction techniques using two different image databases. The results demonstrated that the proposed techniques improve the performance of the image retrieval by reducing up to 50% the feature vector dimensionality of SIFT and at the same time providing a gain of computational time of modeling an image employing Bag-of-Features.
Starting Page	139
Ending Page	142
Page Count	4
File Format	PDF
ISBN	9781450317061
DOI	10.1145/2382636.2382668
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Feature selection Sift Bag-of-features Image retrieval
Content Type	Text
Resource Type	Article

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