A saliency map is a model that predicts eye fixations on a visual scene. Saliency prediction is informed by the human visual attention mechanism and predicts the possibility of the human eyes to stay in a certain position in the scene.
데이터셋
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The SALIency in CONtext (SALICON) dataset contains 10,000 training images, 5,000 validation images and 5,000 test images for saliency prediction. This dataset has been created by annotating saliency in images from MS COCO. The ground-truth saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded. The training and validation sets, provided with ground truth, contain the following data fields: image, resolution and gaze. The testing data contains only the image and resolution fields. Source: DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations Image Source: http://salicon.net/explore/
Includes 950 real-world underwater images, 890 of which have the corresponding reference images. Source: An Underwater Image Enhancement Benchmark Dataset and Beyond
The Segmentation of Underwater IMagery (SUIM) dataset contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants. Source: Semantic Segmentation of Underwater Imagery: Dataset and Benchmark Image Source: http://irvlab.cs.umn.edu/resources/suim-dataset
The dataset presents open high-resolution test clips set with different types of content: movie fragments, sport streams, live caption clips. Used clips of 1920×1080 resolution and with duration from 13 to 38 seconds. And Performed reliable data collection from 50 observers (19–24 y. o.) using 500 Hz SMI iViewXTM Hi-Speed 1250 eye-tracker. Also used cross-fade which ensures the independence of the received fixations between different clips. The final ground-truth saliency map was estimated as a Gaussian mixture with centers at the fixation points. A standard deviation for the Gaussians equal to 120 was chosen (this value matches 8 angular degrees, which is known to be the sector of sharp vision).
A novel audio-visual mouse saliency (AViMoS) dataset with the following key-features: * Diverse content: movie, sports, live, vertical videos, etc.; * Large scale: 1500 videos with mean 19s duration; * High resolution: all streams are FullHD; * Audio track saved and played to observers; * Mouse fixations from >5000 observers (>70 per video); * License: CC-BY;
The CapMIT1003 database contains captions and clicks collected for images from the MIT1003 database, for which reference eye scanpath are available. The database is distributed as a single SQLite3 database named capmit1003.db. For convenience, a lightweight Python class to access the database is provided in the official repository