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Title: DeepScores : a dataset for segmentation, detection and classification of tiny objects
Authors : Tuggener, Lukas
Elezi, Ismail
Schmidhuber, Jürgen
Pelillo, Marcello
Stadelmann, Thilo
Proceedings: Proceedings of the 24th International Conference on Pattern Recognition
Pages : 1
Pages to: 6
Conference details: 24th International Conference on Pattern Recognition (ICPR 2018), Beijing, China, 20-28 August 2018
Publisher / Ed. Institution : IAPR
Publisher / Ed. Institution: Beijing
Issue Date: Aug-2018
License (according to publishing contract) : Licence according to publishing contract
Type of review: Open peer review
Language : English
Subjects : Optical music recognition; Deep learning; Computer vision; Data set
Subject (DDC) : 005: Computer programming, programs and data
Abstract: We present the DeepScores dataset with the goal of advancing the state-of-the-art in small object recognition by placing the question of object recognition in the context of scene understanding. DeepScores contains high quality images of musical scores, partitioned into 300,000 sheets of written music that contain symbols of different shapes and sizes. With close to a hundred million small objects, this makes our dataset not only unique, but also the largest public dataset. DeepScores comes with ground truth for object classification, detection and semantic segmentation. DeepScores thus poses a relevant challenge for computer vision in general, and optical music recognition (OMR) research in particular. We present a detailed statistical analysis of the dataset, comparing it with other computer vision datasets like PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, as well as with other OMR datasets. Finally, we provide baseline performances for object classification, intuition for the inherent difficulty that DeepScores poses to state-of-the-art object detectors like YOLO or R-CNN, and give pointers to future research based on this dataset.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Conference Paper
DOI : 10.21256/zhaw-4255
Published as part of the ZHAW project : DeepScore: Digitales Notenpult mit musikalischem Verständnis durch Active Sheet Technologie
Appears in Collections:Publikationen School of Engineering

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