SALICON

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on SALICON

SALICON:

This image dataset which is also a mouse tracking dataset, has been created from a subset of images from a parent dataset called MS COCO 2014 (available on http://cocodataset.org/#home) with an additional annotation type “fixation”. The visual attentional data for this dataset is collected by using mouse tracking methods. The research work related to this dataset aimed to find answers about human visual attention and decision making. In their paper which is available in bellow, they evaluated their mouse tracking method by comparing the results with eye-tracking.

Here is some information regarding the latest version of this dataset:

  • Number of images in the dataset: 20,000 (10,000 images for training set, 5000 images for validation, 5000 for test set)

  • Number of classes: 80

  • Image resolution: 640×480

More details and links for download can be found on the dataset page http://salicon.net/ and SALICON challenge 2017 page http://salicon.net/challenge-2017/.

You might also be interested to use the SALICON API Python package available on GitHub https://github.com/NUS-VIP/salicon-api.

If you use this dataset:

Please make sure to read Terms of Use available on http://salicon.net/challenge-2017/.

Please make sure to cite the paper:

M. Jiang, S. Huang, J. Duan, Q. Zhao, SALICON: Saliency in Context. CVPR 2015.

keywords: Vision, Image, Classification, Scene, Saliency Analysis

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SUN

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on SUN

SUN:

This dataset contains thousands of color images for scenes recognition provided by Princeton University. The images include environmental scenes, places and objects. To create the dataset, WordNet English dictionary is used to find any nouns completing the sentence “I am in -a place-“ or “Let’s go to -the place-“ and data samples are manually categorized. The number of images per category are different for this dataset with the minimum of 100 images per category for the LSUN397 version.

Different versions are available for the dataset. Here is some information about LSUN397 dataset:

  • Number of images in the dataset: 16,873

  • Number of classes: 397 (Abbey, Access_road, etc.)

Here is some information regarding the latest version of this dataset:

  • Number of images in the dataset: 131,067

  • Number of classes: 908 scene categories and 3819 object categories

More details and links of download are available on the dataset pages https://vision.princeton.edu/projects/2010/SUN/ and https://groups.csail.mit.edu/vision/SUN/. Recommendations for training and testing split are also available in the mentioned pages.

If you use this dataset, make sure to cite these two papers:

J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. Sun Database: Large-scale Scene Recognition from Abbey to Zoo, IEEE Conference on Computer Vision and Pattern Recognition, 2010.

J. Xiao, K. A. Ehinger, J. Hays, A. Torralba, and A. Oliva, Sun Database: Exploring a Large Collection of Scene Categories. International Journal of Computer Vision (IJCV), 2014.

keywords: Vision, Image, Classification, Scene, Object Detection

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LSUN

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on LSUN

LSUN:

This dataset contains millions of color images for scenes and objects which is far bigger than ImageNet dataset. The labels for this dataset are available based on human’s effort for labeling in conjunction with several different image classification models. The images are from parent databases Pascal Voc 2012 and 10 Million Images for 10 Scene Categories.

Here is some information regarding the LSUN dataset:

  • Number of images in the dataset: More than 59 million and still growing

  • Number of classes: 10 scene categories and 20 object categories

  1. Scene categories (bedroom, bridge, church_outdoor, classroom, conference_room, dining_room, kitchen, living_room, restaurant, tower)

20 object categories (airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining_table, dog, horse, motorbike, person, potted_plant, sheep, sofa, train, tv-monitor)

The dataset can be downloaded either from GitHub https://github.com/fyu/lsun or the categories lists on http://tigress-web.princeton.edu/~fy/lsun/public/release/. More details are available on the dataset page http://www.yf.io/p/lsun.

If you use this dataset, make sure to cite the paper:

Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser and Jianxiong Xiao. Corr, LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. abs/1506.03365, 2015

keywords: Vision, Image, Classification, Scene, Object Detection

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