Effective Image Database Search via Dimensionality Reduction

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2008

Standard

Effective Image Database Search via Dimensionality Reduction. / Dahl, Anders Bjorholm; Aanæs, Henrik.

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Anchorage, Alaska : IEEE, 2008. p. 1-6.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2008

Harvard

Dahl, AB & Aanæs, H 2008, Effective Image Database Search via Dimensionality Reduction. in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, Anchorage, Alaska, pp. 1-6. DOI: 10.1109/CVPRW.2008.4562957

APA

Dahl, A. B., & Aanæs, H. (2008). Effective Image Database Search via Dimensionality Reduction. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 1-6). Anchorage, Alaska: IEEE. DOI: 10.1109/CVPRW.2008.4562957

CBE

Dahl AB, Aanæs H. 2008. Effective Image Database Search via Dimensionality Reduction. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Anchorage, Alaska: IEEE. pp. 1-6. Available from: 10.1109/CVPRW.2008.4562957

MLA

Dahl, Anders Bjorholm and Henrik Aanæs "Effective Image Database Search via Dimensionality Reduction". 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Anchorage, Alaska: IEEE. 2008. 1-6. Available: 10.1109/CVPRW.2008.4562957

Vancouver

Dahl AB, Aanæs H. Effective Image Database Search via Dimensionality Reduction. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Anchorage, Alaska: IEEE. 2008. p. 1-6. Available from, DOI: 10.1109/CVPRW.2008.4562957

Author

Dahl, Anders Bjorholm; Aanæs, Henrik / Effective Image Database Search via Dimensionality Reduction.

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Anchorage, Alaska : IEEE, 2008. p. 1-6.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2008

Bibtex

@inbook{095182bb55d647a18c59e1cd56166be4,
title = "Effective Image Database Search via Dimensionality Reduction",
abstract = "Image search using the bag-of-words image representation is investigated further in this paper. This approach has shown promising results for large scale image collections making it relevant for Internet applications. The steps involved in the bag-of-words approach are feature extraction, vocabulary building, and searching with a query image. It is important to keep the computational cost low through all steps. In this paper we focus on the efficiency of the technique. To do that we substantially reduce the dimensionality of the features by the use of PCA and addition of color. Building of the visual vocabulary is typically done using k-means. We investigate a clustering algorithm based on the leader follower principle (LF-clustering), in which the number of clusters is not fixed. The adaptive nature of LF-clustering is shown to improve the quality of the visual vocabulary using this. In the query step, features from the query image are assigned to the visual vocabulary. The dimensionality reduction enables us to do exact feature labeling using kD-tree, instead of approximate approaches normally used. Despite the dimensionality reduction to between 6 and 15 dimensions we obtain improved results compared to the traditional bag-of-words approach based on 128 dimensional SIFT feature and k-means clustering.",
keywords = "object recognition, bag-of-words model, color SIFT features",
author = "Dahl, {Anders Bjorholm} and Henrik Aanæs",
note = "Copyright: 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE",
year = "2008",
doi = "10.1109/CVPRW.2008.4562957",
isbn = "14-24-42339-2",
pages = "1--6",
booktitle = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Effective Image Database Search via Dimensionality Reduction

AU - Dahl,Anders Bjorholm

AU - Aanæs,Henrik

N1 - Copyright: 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

PY - 2008

Y1 - 2008

N2 - Image search using the bag-of-words image representation is investigated further in this paper. This approach has shown promising results for large scale image collections making it relevant for Internet applications. The steps involved in the bag-of-words approach are feature extraction, vocabulary building, and searching with a query image. It is important to keep the computational cost low through all steps. In this paper we focus on the efficiency of the technique. To do that we substantially reduce the dimensionality of the features by the use of PCA and addition of color. Building of the visual vocabulary is typically done using k-means. We investigate a clustering algorithm based on the leader follower principle (LF-clustering), in which the number of clusters is not fixed. The adaptive nature of LF-clustering is shown to improve the quality of the visual vocabulary using this. In the query step, features from the query image are assigned to the visual vocabulary. The dimensionality reduction enables us to do exact feature labeling using kD-tree, instead of approximate approaches normally used. Despite the dimensionality reduction to between 6 and 15 dimensions we obtain improved results compared to the traditional bag-of-words approach based on 128 dimensional SIFT feature and k-means clustering.

AB - Image search using the bag-of-words image representation is investigated further in this paper. This approach has shown promising results for large scale image collections making it relevant for Internet applications. The steps involved in the bag-of-words approach are feature extraction, vocabulary building, and searching with a query image. It is important to keep the computational cost low through all steps. In this paper we focus on the efficiency of the technique. To do that we substantially reduce the dimensionality of the features by the use of PCA and addition of color. Building of the visual vocabulary is typically done using k-means. We investigate a clustering algorithm based on the leader follower principle (LF-clustering), in which the number of clusters is not fixed. The adaptive nature of LF-clustering is shown to improve the quality of the visual vocabulary using this. In the query step, features from the query image are assigned to the visual vocabulary. The dimensionality reduction enables us to do exact feature labeling using kD-tree, instead of approximate approaches normally used. Despite the dimensionality reduction to between 6 and 15 dimensions we obtain improved results compared to the traditional bag-of-words approach based on 128 dimensional SIFT feature and k-means clustering.

KW - object recognition

KW - bag-of-words model

KW - color SIFT features

U2 - 10.1109/CVPRW.2008.4562957

DO - 10.1109/CVPRW.2008.4562957

M3 - Article in proceedings

SN - 14-24-42339-2

SP - 1

EP - 6

BT - 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

PB - IEEE

ER -