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The knowing camera 2: recognizing and annotating places-of-interest in smartphone photos

Published: 03 July 2014 Publication History

Abstract

This paper presents a project called Knowing Camera for real-time recognizing and annotating places-of-interest(POI) in smartphone photos, with the availability of online geotagged images of such places. We propose a`"Spatial+Visual" (S+V) framework which consists of a probabilistic field-of-view model in the spatial phase and sparse coding similarity metric in the visual phase to recognize phone-captured POIs. Moreover, we put forward an offline Collaborative Salient Area (COSTAR) mining algorithm to detect common visual features (called Costars) among the noisy photos geotagged on each POI, thus to clean the geotagged image database. The mining result can be utilized to annotate the region-of-interest on the query image during the online query processing. Besides, this mining procedure further improves the efficiency and accuracy of the S+V framework. Our experiments in the real-world and Oxford 5K datasets show promising recognition and annotation performances of the proposed approach, and that the proposed COSTAR mining technique outperforms state-of-the-art approach.

References

[1]
http://www.cs.umd.edu/~mount/ann/.
[2]
http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/.
[3]
R. Achanta, F. J. Estrada, P. Wils, and S. Süsstrunk. Salient region detection and segmentation. In ICVS, volume 5008 of Lecture Notes in Computer Science, pages 66--75. Springer, 2008.
[4]
Y. S. Avrithis, Y. Kalantidis, G. Tolias, and E. Spyrou. Retrieving landmark and non-landmark images from community photo collections. In ACM Multimedia, pages 153--162. ACM, 2010.
[5]
D. M. Chen, G. Baatz, K. Koser, S. S. Tsai, R. Vedantham, T. Pylvanainen, K. Roimela, X. Chen, J. Bach, M. Pollefeys, B. Girod, and R. Grzeszczuk. City-scale landmark identification on mobile devices. In CVPR, pages 737--744. IEEE, 2011.
[6]
O. Chum, A. Mikulík, M. Perdoch, and J. Matas. Total recall ii: Query expansion revisited. In CVPR, pages 889--896. IEEE, 2011.
[7]
O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman. Total recall: Automatic query expansion with a generative feature model for object retrieval. In ICCV, pages 1--8. IEEE, 2007.
[8]
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. Least angle regression. The Annals of Statistics, 32(2):407--451, 2004.
[9]
H. Jégou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. In European Conference on Computer Vision, volume~I of LNCS, pages 304--317. Springer, oct 2008.
[10]
H. Jegou, M. Douze, and C. Schmid. Improving bag-of-features for large scale image search. International Journal of Computer Vision, 87(3):316--336, 2010.
[11]
L. Juan and O. Gwon. A Comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing (IJIP), 3(4):143--152.
[12]
X. Li, C. Wu, C. Zach, S. Lazebnik, and J.-M. Frahm. Modeling and recognition of landmark image collections using iconic scene graphs. In ECCV (1), volume 5302 of Lecture Notes in Computer Science, pages 427--440. Springer, 2008.
[13]
T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum. Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell., 33(2):353--367, 2011.
[14]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60:91--110, 2004.
[15]
D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In CVPR (2), pages 2161--2168. IEEE Computer Society, 2006.
[16]
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007.
[17]
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In CVPR. IEEE Computer Society, 2008.
[18]
J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In ICCV, pages 1470--1477. IEEE Computer Society, 2003.
[19]
P. Turcot and D. G. Lowe. Better matching with fewer features: The selection of useful features in large database recognition problems. In ICCV Workshop on Emergent Issues in Large Amounts of Visual Data (WS-LAVD), 2009.
[20]
T. Tuytelaars and K. Mikolajczyk. Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision, 3(3):177--280, 2007.
[21]
J. Wang, C. Zhang, Y. Zhou, Y. Wei, and Y. Liu. Global contrast of superpixels based salient region detection. In CVM, volume 7633 of Lecture Notes in Computer Science, pages 130--137. Springer, 2012.
[22]
M. Z. Zheng, Yan-Tao, Y. Song, H. Adam, U. Buddemeier, A. Bissacco, F. Brucher, T.-S. Chua, and H. Neven. Tour the world: Building a web-scale landmark recognition engine. In CVPR, pages 1085--1092. IEEE, 2009.

Cited By

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  • (2019)One net to rule them all: efficient recognition and retrieval of POI from geo-tagged photosMultimedia Tools and Applications10.1007/s11042-018-6847-yOnline publication date: 4-Jan-2019
  • (2019)Your clicks reveal your secretsMultimedia Tools and Applications10.1007/s11042-018-6815-678:7(8337-8362)Online publication date: 1-Apr-2019
  • (2018)Spotlight: Multiple-Object Localization by Mobile Photo Fusion2018 4th International Conference on Big Data Computing and Communications (BIGCOM)10.1109/BIGCOM.2018.00044(231-235)Online publication date: Aug-2018
  • Show More Cited By

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  1. The knowing camera 2: recognizing and annotating places-of-interest in smartphone photos

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    cover image ACM Conferences
    SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
    July 2014
    1330 pages
    ISBN:9781450322577
    DOI:10.1145/2600428
    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]

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    Published: 03 July 2014

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    Author Tags

    1. image recognition
    2. location-based service
    3. places-of-interest

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    SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

    View all
    • (2019)One net to rule them all: efficient recognition and retrieval of POI from geo-tagged photosMultimedia Tools and Applications10.1007/s11042-018-6847-yOnline publication date: 4-Jan-2019
    • (2019)Your clicks reveal your secretsMultimedia Tools and Applications10.1007/s11042-018-6815-678:7(8337-8362)Online publication date: 1-Apr-2019
    • (2018)Spotlight: Multiple-Object Localization by Mobile Photo Fusion2018 4th International Conference on Big Data Computing and Communications (BIGCOM)10.1109/BIGCOM.2018.00044(231-235)Online publication date: Aug-2018
    • (2018)State-of-Art ResearchesSensing Vehicle Conditions for Detecting Driving Behaviors10.1007/978-3-319-89770-7_5(65-68)Online publication date: 19-Apr-2018
    • (2016)KISSIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.248964728:4(994-1006)Online publication date: 1-Apr-2016
    • (2015)DeepCameraProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806620(1891-1894)Online publication date: 17-Oct-2015
    • (2015)E2C2Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers10.1145/2800835.2800841(9-12)Online publication date: 7-Sep-2015
    • (2015)Image-Based Recommendations on Styles and SubstitutesProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767755(43-52)Online publication date: 9-Aug-2015

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