Tour the world building a web scale landmark recognition engine
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Tour the World: building a web-scale landmark recognition engine. ICCV 2009 Yan-Tao Zheng1, Ming Zhao2, Yang Song2, Hartwig Adam2 Ulrich Buddemeier2, Alessandro Bissacco2, Fernando Brucher2 Tat- Seng Chua1, and Hartmut Neven2. Outline. Introduction Methods Experiments & Results

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Tour the World: building a web-scale landmark recognition engine

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Tour the world building a web scale landmark recognition engine

Tour the World: building a web-scale landmark recognition engine

ICCV 2009

Yan-Tao Zheng1, Ming Zhao2, Yang Song2, Hartwig Adam2

Ulrich Buddemeier2, Alessandro Bissacco2, Fernando Brucher2

Tat-Seng Chua1, and Hartmut Neven2


Outline

Outline

  • Introduction

  • Methods

  • Experiments & Results

  • Conclusion


Introduction

Introduction


Introduction1

Introduction

  • Motivation

    • the vast amount of landmark images in the Internet

  • Applications

    • clean landmark images for building virtual tourism

    • content understanding and geo-location detection

    • provide tour guide recommendation and visualization

  • Issues

    • no readily available list of landmarks in the world

    • even if there were such a list, it is still challenging to collect true landmark images

    • efficiency for such a large-scale system.


Discovering landmarks in the world

Discovering Landmarks in the World

  • Comprehensive and well-organized list of landmarks

  • Twosources on the Internet

    • Geographically calibratedimages in photo sharing websites(ex: Picasa)

      • Geo-tag, text-tag, popularity

    • Travel guide articles from websites(ex: wikitravel)

      • Text-based


Frameworks

Frameworks

Agglomerative hierarchical clustering

Validation criterion:

The number of authors in cluster > threshold

Google image search & Google map

Extract landmark names from the city tour guide corpus[16]

[16] J. Yi and N. Sundaresan. A classifier for semi-structured documents. In Proc. of Conf. on Knowledge Discovery and Data Mining, pages 340–344, New York, NY, USA, 2000. 2, 3


Landmarks mined from gps tagged

Landmarks mined from GPS-tagged


Landmarks extracted from tour guide

Landmarks extracted from tour guide


Frameworks1

Frameworks

SIFT-128 -> PCA -> feature dimension 40

Affine transformation[9]

[9] D. Lowe. Distinctive image features from scale-invariant keypoints. In International Journal of Computer Vision, volume 20, pages 91–110, 2003.


Graph cluster

Graph cluster


Frameworks2

Frameworks

Validation criterion:

1. Landmark name too long or most of its words are not capitalized

2. The number of authors in cluster > threshold

Cleaning:

1. Photographic vs non-photographic classifier(Adaboost algorithm)

2. Face detector[15]

[15] P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features.

In Proc. of Conf. on Computer Vision and Pattern Recognition, volume 1, pages I–511–I–518 vol.1, 2001.


Photographic vs non photographic classifier

Photographic vs non-photographic classifier


Efficiency issues

Efficiency Issues

  • Parallel computing to mine true landmark images

  • Efficiency in hierarchical clustering

  • Indexing local feature for matching

    • Query, k-d tree


Experiments results

Experiments & Results

  • ~20 million GPS-tagged photos from picasa and panoramio to construct geo-cluster

  • Mined from tour guide corpus in Google Image Search to construct noisy image set from first 200 returned images.

  • The total number of images ~21.4 million.


Statistics of mined landmarks

Statistics of mined landmarks

  • GPS-tagged photos delivers

    • 2240 validatedlandmarks, from 812 cities in 104 countries.

  • The tourguide corpus yields

    • 3246 validated landmarks, from 626cities in 130 ountries.

  • Only 174 landmarksin both list


Statistics of mined landmarks1

Statistics of mined landmarks

  • The combined list of landmarks consists of 5312 uniquelandmarks from 1259 cities in 144 countries.


Statistics of mined landmarks2

Statistics of mined landmarks

  • Our processing language focuseson English only.

  • Thenumber of landmarks in China amounts to 101 only->a multi-lingual data mining task


Evaluation of landmark image mining

Evaluation of landmark image mining

  • set the minimum cluster size to 4

  • The visual clustering yields

    • ~14k visual clusters with ~800k images for landmarks mined from GPS-tagged photos

    • ~12k clusters with ~110k images for landmarks mined from tour guide corpus.

  • 1000 visual clustersare randomly selected to evaluate the correctness

    • Outliercluster rate 0.68% ( 68 out of 1000)

    • By validation and cleaning, dropsto 0.37%(37 out of 1000).


Evaluation of landmark recognition

Evaluation of landmark recognition

  • Positive and Negative query images

  • Positive testing image set consists of 728 images from 124 randomly selected landmarks

    • manually annotated from images that range from 201 to 300 in the Google Image Search


Evaluation of landmark recognition1

Evaluation of landmark recognition

  • Negative testing set consists of 30524 (Caltech-256) + 9986 (Pascal VOC 07) = 40510 images in total

  • The recognition by local feature matching of query image against model images

    • nearest neighbor (NN) principle.


Evaluation of landmark recognition2

Evaluation of landmark recognition

  • Positive testing image set, 417 images are detected by the system to be landmarks, of which 337 are correctly identified. The accuracy of identification is 80.8%, which is fairly satisfactory, considering the large number of landmark models in the system.

  • This high accuracy enables our system to provide landmark recognition to other applications, like image content analysis and geo-location detection. The identification rate (correctly identified / positive testing images) is 46.3% (337/728)


Evaluation of landmark recognition3

Evaluation of landmark recognition


Evaluation of landmark recognition4

Evaluation of landmark recognition

  • For the negative testing set, 463 out of 40510 images

  • The false acceptance rate is only 1.1%

  • After careful examination, we find that most false matches occur in two scenarios:

    • (1) the match is technically correct, but the match region is not representative to the landmark; and

    • (2) the match is technically false, due to the visual similarity between negative images and landmark.


Conclusion

Conclusion

  • We build a world-scale landmark recognition engine with a multi-source and multi-modal data mining task.

  • We have employed the GPS-tagged photos and online tour guide corpus to generate a worldwide landmark list.

  • We then utilize ~21.4 M images to build up landmark visual models, in an unsupervised fashion.

  • The experiments demonstrate that the engine can deliver satisfactory recognition performance, with high efficiency.


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