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  9. FlickrLogos

FlickrLogos

FlickrLogos

aldi
? Universit?t Augsburg
adidas
? Universit?t Augsburg
apple
? Universit?t Augsburg
becks
? Universit?t Augsburg
bmw
? Universit?t Augsburg
carlsberg
? Universit?t Augsburg
chimay
? Universit?t Augsburg
cocacola
? Universit?t Augsburg
corona
? Universit?t Augsburg
dhl
? Universit?t Augsburg
erdinger
? Universit?t Augsburg
esso
? Universit?t Augsburg
fedex
? Universit?t Augsburg
ferrari
? Universit?t Augsburg
ford
? Universit?t Augsburg
fosters
? Universit?t Augsburg
google
? Universit?t Augsburg
guiness
? Universit?t Augsburg
heineken
? Universit?t Augsburg
hp
? Universit?t Augsburg
milka
? Universit?t Augsburg
nvidia
? Universit?t Augsburg

The FlickrLogos dataset consists of real-world images collected from Flickr depicting company logos in various circumstances. The dataset comes in two versions: The original FlickrLogos-32 dataset and the FlickrLogos-47 dataset. FlickrLogos-32 was designed for logo retrieval and multi-class logo detection and object recognition. However, the annotations for object detection were often incomplete,since only the most prominent logo instances were labelled. FlickrLogos-47 uses the same image corpus as FlickrLogos-32 but has been re-annotated specifically for the task of object detection and recognition. New classes were introduced (i.e. the company logo and text are now treated as separate classes where applicable) and missing object instances have been annotated. Furthermore, we provide corresponsing evaluation scripts.

FlickrLogos-32

Description:

The dataset FlickrLogos-32 contains photos showing brand logos and is meant for the evaluation of logo retrieval and multi-class logo detection/recognition systems on real-world images. We collected logos of 32 different logo brands by downloading them from Flickr. All logos have an approximately planar surface.

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Classes:

There are 32 logo classes: Adidas, Aldi, Apple, Becks, BMW, Carlsberg, Chimay, Coca-Cola, Corona, DHL, Erdinger, Esso, Fedex, Ferrari, Ford, Foster's, Google, Guiness, Heineken, HP, Milka, Nvidia, Paulaner, Pepsi, Ritter Sport, Shell, Singha, Starbucks, Stella Artois, Texaco, Tsingtao and UPS.

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Partitions / subsets:

The retrieved images were inspected manually to ensure that the specific logo is actually shown. The whole dataset is split into three disjoint subsets P1, P2, and P3, each containing images of all 32 classes. The first partition P1 - the training set - consists of 10 images that were hand-picked such that these consistently show a single logo under various views with as little background clutter as possible. The other two partitions P2 (validation set) and P3 (test set = query set) contain 30 images per class. Unlike P1 these images contain at least one instance of a logo but in several cases multiple instances.

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To facilitate the development of high-precision classifiers the evaluation of their sensitivity on non-logo images is important. Therefore both partitions P2, and P3 include another 3000 images downloaded from Flickr with the queries "building", "nature", "people" and "friends". These images are the negative images and complete our dataset. A brief summary of the data subsets is shown in the table below.

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Dataset Partitions / Subsets
Partition Description Images #Images
P1 (training set)

Hand-picked images

 10 per class 320 images
P2 (validation set) Images showing at least a single logo under various views 30 per class 3960 images
Non-logo images 3000
P3 (test set = query set) Images showing at least a single logo under various views 30 per class 3960 images
Non-logo images 3000
P1, P2 and P3 are disjoint. ? 8240 images

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Pixel-level annotations

This dataset further includes pixel-level annotations, i.e. binary masks + bounding boxes (see above) that mark the position of the logo in each image.

The binary masks are provided as .png files as well as by the coordinates of the corresponding bounding box.
Each logo image in <basedir>/classes/jpg/<class>/<file>.jpg has its mask in <basedir>/classes/masks/<class>/<file>.mask.<n>.png where n is the mask number starting at 0.
Masks are single-channel PNG images of the same size as the original image. Masked areas (where the logos is) have a value != 0, unmasked areas (=background) 0.

There are separate masks for each annotation in an image available and an additional mask merged from all individual masks:
Each logo image in <basedir>/classes/jpg/<class>/<file>.jpg has its merged mask in <basedir>/classes/masks/<class>/<file>.mask.merged.png.

Also thumbnail images are included for easy visualization of results.

Evaluation Protocol

Evaluation of classification/recognition methods:
  • Training may use training + validation set only.
  • All the images in the test set P3 (logos+non-logos = 3960 images) are used for the evaluation of recognition methods.

Evaluation of retrieval methods:

  • All images in training and validation set are indexed, including non-logo images = 4280 images in total.
  • All images in query set P3 (logos only = 960 images) are used for the evaluation of retrieval methods.
  • The average precision is computed for every query and averaged over all queries yielding the final mAP.
  • If not specified otherwise the masks/bounding boxes are not used as region-of-interest when indexing and querying.

Evaluation & Tools

We provide evaluation scripts to test both retrieval and classification systems (Python 2.7+ needed):
  • fl_eval_retrieval.py: Evaluates the retrieval of logo images by computing mAP, AvgTop4 score, response ratio, etc.
  • fl_eval_classification.py:: Evaluates the classification of logo images by computing precision, recall and more.
  • fl_plot_classification_results.py: Plots classification results, i.e. true positives per class and the confusion matrix. Contained in full package.
  • Other scripts that simplify the management of this dataset (copying, etc..)
Download the evaluation kit including all scripts plus additional sample data. The evaluation kit was last updated on 18th November 2013 (Version 1.0.4). If you encounter problems, please contact us.

Paper

If you use this dataset in your work please cite the following paper:

[1] Scalable Logo Recognition in Real-World Images
Stefan Romberg, Lluis Garcia Pueyo, Rainer Lienhart, Roelof van Zwol
ACM International Conference on Multimedia Retrieval 2011 (ICMR11), Trento, April 2011.
Also Technical Report, 伟德国际_伟德国际1946$娱乐app游戏 of Augsburg, Institute of Computer Science, March 2011

FlickrLogos-47

Description:

The dataset FlickrLogos-47 contains photos showing brand logos and is meant for the evaluation of logo detection and recognition systems on real-world images. It is built from the same images than the FlickrLogos-32 dataset which have been re-annotated to fix mission annotations and to include more classes.

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Classes:

There are 47 logo classes: Adidas (Symbol), Adidas (Text), Aldi, Apple, Becks (Symbol), Becks (Text), BMW, Carlsberg (Symbol), Carlsberg (Text), Chimay (Symbol), Chimay (Text), Coca-Cola, Corona (Symbol), Corona (Text), DHL, Erdinger (Symbol), Erdinger (Text), Esso (Symbol), Esso (Text), Fedex, Ferrari, Ford, Foster's (Symbol), Foster's (Text), Google, Guiness (Symbol), Guiness (Text), Heineken, HP, Milka (Symbol), Milka (Text), Nvidia (Symbol), Nvidia (Text), Paulaner (Symbol), Paulaner (Text), Pepsi (Symbol), Pepsi (Text), Ritter Sport, Shell, Singha (Symbol), Singha (Text), Starbucks, Stella Artois (Symbol), Stella Artois (Text), Texaco, Tsingtao (Symbol) Tsingtao (Text) and UPS.

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Download

Download

If you wish to download one (or both of the datasets), please send an (informal) email to request_flickrlogos@informatik.uni-augsburg.de. Please state your name, your institution and why you would like to have access to this dataset (we are curious). We then send you a download link by e-mail.

Note: This dataset consists of images downloaded from Flickr. Use of these images must respect Flickr's terms of use.

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Important Notes

There is a similar dataset called FlickrLogos-27. It consists of 27 classes only and while there is some overlap it is largely different from our FlickrLogos-32 dataset. Results for these different datasets are not comparable.

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Contact

If you have any questions, corrections or other issues please contact Stephan Brehm. The former maintainers were Christian Eggert and Stefan Romberg.

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