short-paper

Augmented Feature Fusion for Image Retrieval System

Authors:

Shiliang Zhang,

Qi TianAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 447 - 450

https://doi.org/10.1145/2671188.2749288

Published: 22 June 2015 Publication History

Abstract

The performance of current image retrieval system is largely determined by the quality and discriminative capability of features. Therefore, using what features and how to effectively combine the power of appropriate features are important in the system. We adopt the reciprocal neighbor based graph fusion approach for feature fusion. More importantly, we explicitly augment the original approach with the following two strategies: 1) we investigate the most suitable feature combinations on various datasets, including the deep learning feature, which has been popular for image retrieval recently; 2) we further improve the robustness of original graph fusion approach by the SVM prediction strategy.

Extensive experiments are performed on three benchmark datasets including UKbench, Holidays and Corel-5K, to validate the impressive performance of the augmented feature fusion. On the three datasets, our retrieval system significantly outperforms several existing algorithms. For example on UKbench, the N-S score of our approach achieves 3.88, which is one of the highest accuracies to the best of our knowledge.

References

[1]

D. Cai, X. He, and J. Han. Spectral regression: a unified subspace learning framework for content-based image retrieval. In MM, pages 403--412, 2007.

Digital Library

[2]

R. Fagin, R. Kumar, and D. Sivakumar. Efficient similarity search and classification via rank aggregation. In SIGMOD, pages 301--312, 2003.

Digital Library

[3]

P. Gehler and S. Nowozin. On feature combination for multiclass object classification. In ICCV, pages 221--228, 2009.

[4]

Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In CVPR, pages 817--824, 2011.

Digital Library

[5]

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. SPM, 29(6):82--97, 2012.

[6]

H. Jégou, M. Douze, and C. Schmid. On the burstiness of visual elements. In CVPR, pages 1169--1176, 2009.

[7]

H. Jégou, M. Douze, and C. Schmid. Improving bag-of-features for large scale image search. IJCV, 87(3):316--336, 2010.

Digital Library

[8]

H. Jégou, C. Schmid, H. Harzallah, and J. Verbeek. Accurate image search using the contextual dissimilarity measure. T-PAMI, 32(1):2--11, 2010.

Digital Library

[9]

Y. Jia. Caffe: An open source convolutional architecture for fast feature embedding. http://caffe.berkeleyvision.org/, 2013.

[10]

A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, volume 1, page 4, 2012.

Digital Library

[11]

Y. LeCun, K. Kavukcuoglu, and C. Farabet. Convolutional networks and applications in vision. In ISCAS, pages 253--256, 2010.

[12]

D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91--110, 2004.

Digital Library

[13]

D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR, pages 2161--2168, 2006.

Digital Library

[14]

A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 42(3):145--175, 2001.

Digital Library

[15]

L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, 1999.

[16]

J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, pages 1--8, 2007.

[17]

D. Qin, S. Gammeter, L. Bossard, T. Quack, and L. Van Gool. Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors. In CVPR, pages 777--784. IEEE, 2011.

Digital Library

[18]

D. Qin, C. Wengert, and L. Van Gool. Query adaptive similarity for large scale object retrieval. In CVPR, pages 1610--1617, 2013.

Digital Library

[19]

Q. Tian, N. Sebe, M. S. Lew, E. Loupias, and T. S. Huang. Content-based image retrieval using wavelet-based salient points. In Photonics West 2001-Electronic Imaging, 2001.

[20]

Q. Tian, J. Yu, Q. Xue, and N. Sebe. A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval. In ICME, 2004.

[21]

A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In CVPR, pages 1--8, 2008.

[22]

X. Wang, M. Yang, T. Cour, S. Zhu, K. Yu, and T. X. Han. Contextual weighting for vocabulary tree based image retrieval. In ICCV, pages 209--216, 2011.

Digital Library

[23]

Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, volume 9, page 6, 2008.

Digital Library

[24]

S. Zhang, J. Huang, Y. Huang, Y. Yu, H. Li, and D. N. Metaxas. Automatic image annotation using group sparsity. In CVPR, pages 3312--3319, 2010.

[25]

S. Zhang, M. Yang, T. Cour, K. Yu, and D. N. Metaxas. Query specific fusion for image retrieval. In ECCV, pages 660--673. 2012.

Digital Library

[26]

S. Zhang, M. Yang, X. Wang, Y. Lin, and Q. Tian. Semantic-aware co-indexing for image retrieval. In ICCV, 2013.

Digital Library

Cited By

Lu XZhang LNiu LChen QWang J(2021)A Novel Adaptive Feature Fusion Strategy for Image RetrievalEntropy10.3390/e2312167023:12(1670)Online publication date: 12-Dec-2021
https://doi.org/10.3390/e23121670
Chen ZRajamanickam LCao JZhao AHu X(2021)A localization strategy combined with transfer learning for image annotationPLOS ONE10.1371/journal.pone.026075816:12(e0260758)Online publication date: 8-Dec-2021
https://doi.org/10.1371/journal.pone.0260758
Liu SSun MFeng LQiao HChen SLiu Y(2021)Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image RetrievalIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.298467632:3(1389-1399)Online publication date: Mar-2021
https://doi.org/10.1109/TNNLS.2020.2984676
Show More Cited By

Index Terms

Augmented Feature Fusion for Image Retrieval System
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Image Retrieval Using Fused Deep Convolutional Features

This paper proposes an image retrieval using fused deep convolutional features to solve the semantic gap between low-level features and high-level semantic features of traditional contend-based image retrieval method. Firstly, the improved network ...
A decisive content based image retrieval approach for feature fusion in visual and textual images
Abstract
Image content analysis plays a dynamic role in various computer vision applications. These contents can be either visual (i.e. color, shape, texture) or the textual (i.e. text appearing within images). Both the contents involve ...
Highlights
- A decisive CBIR system is proposed that considers visual and textual contents.
- ...
Weakly Supervised Image Retrieval via Coarse-scale Feature Fusion and Multi-level Attention Blocks
ICMR '19: Proceedings of the 2019 on International Conference on Multimedia Retrieval

In this paper, we propose an end-to-end Attention-Block network for image retrieval (ABIR), which greatly increases the retrieval accuracy without human annotations like bounding boxes. Specifically, our network utilizes coarse-scale feature fusion, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

ICMR '15

Sponsor:

SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

Acceptance Rates

ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
263
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Lu XZhang LNiu LChen QWang J(2021)A Novel Adaptive Feature Fusion Strategy for Image RetrievalEntropy10.3390/e2312167023:12(1670)Online publication date: 12-Dec-2021
https://doi.org/10.3390/e23121670
Chen ZRajamanickam LCao JZhao AHu X(2021)A localization strategy combined with transfer learning for image annotationPLOS ONE10.1371/journal.pone.026075816:12(e0260758)Online publication date: 8-Dec-2021
https://doi.org/10.1371/journal.pone.0260758
Liu SSun MFeng LQiao HChen SLiu Y(2021)Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image RetrievalIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.298467632:3(1389-1399)Online publication date: Mar-2021
https://doi.org/10.1109/TNNLS.2020.2984676
Lu XWang JLi XYang MZhang X(2018)An Adaptive Weight Method for Image Retrieval Based Multi-Feature FusionEntropy10.3390/e2008057720:8(577)Online publication date: 6-Aug-2018
https://doi.org/10.3390/e20080577
Bhowmik NGouet-Brunet VWei LBloch G(2016)Adaptive and Optimal Combination of Local Features for Image RetrievalMultiMedia Modeling10.1007/978-3-319-51814-5_7(76-88)Online publication date: 31-Dec-2016
https://doi.org/10.1007/978-3-319-51814-5_7

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents