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Automated image-orientation detection: a scalable boosting approach

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Abstract

With the proliferation of digital cameras and self-publishing of photos, automatic detection of image orientation has become an important part of photo-management systems. In this paper, we present a novel system, based on combining the outputs of hundreds of classifiers trained with AdaBoost, to determine the upright orientation of an image. We thoroughly test our system on photos gathered from professional and amateur photo collections that have been taken with a variety of cameras (digital, film, camera phones). The test images include photos that are in color and black and white, realistic and abstract, and outdoor and indoor. As this system is intended for mass consumer deployment, efficiency in use and accessibility is paramount. Results show that the presented method surpasses similar methods based on Support Vector Machines, in terms of both accuracy and feasibility of deployment.

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Authors and Affiliations

  1. Google, Inc., 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA

    Shumeet Baluja

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  1. Shumeet Baluja
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Correspondence to Shumeet Baluja.

Appendix 1

Appendix 1

In this section, we give the specific results, per directory, for the large Corel-test set. Table 3 provides the results when considering four orientations and Table 4 provides the results when considering three orientations. Note that in each case, the top performing categories are largely outdoor photographs with very clear orientations. The worst performing categories are abstracts, textures, and backgrounds; these contain little or no orientation information. The ‘Army’ category which appears as the worst performer in both the three and four orientation case consisted of images that were four indoor machinery images, a single one which contained a person, and an additional single outdoor sunset photo.

Table 3 Detailed performance on Corel test set when considering 4-orientations
Full size table
Table 4 Detailed performance on Corel test set when considering 3-orientations
Full size table

When comparing Tables 3 and 4, as expected, many more categories are classified with higher accuracy in Table 4 (3 orientations). Only 17 of the 180 categories had the same performance between the 3 and 4 orientations (all of the others improved). Of the 17 categories, 4 did not improve because the performance was already 100%; the remaining 13 are listed below.

  1. 1.

    Architecture_VIII (87/89)

  2. 2.

    Contemporary_Buildings (82/90)

  3. 3.

    British_Royalty (8/9)

  4. 4.

    Architecture_III (58/66)

  5. 5.

    Beaches (28/32)

  6. 6.

    Children_II (5/6)

  7. 7.

    Berlin (27/34)

  8. 8.

    Dance (6/8)

  9. 9.

    Couples_II (16/23)

  10. 10.

    EMS_Rescue (2/4)

  11. 11.

    Cowboys (7/14)

  12. 12.

    Costumes (1/2)

  13. 13.

    Army (1/5)

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Baluja, S. Automated image-orientation detection: a scalable boosting approach. Pattern Anal Applic 10, 247–263 (2007). https://doi.org/10.1007/s10044-006-0059-1

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  • DOI: https://doi.org/10.1007/s10044-006-0059-1

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