LabelMe

LabelMe: A Database and Web-Based Tool for Image Annotation. We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web


References in zbMATH (referenced in 44 articles )

Showing results 1 to 20 of 44.
Sorted by year (citations)

1 2 3 next

  1. Marcos Nieto, Orti Senderos, Oihana Otaegui: Boosting AI applications: Labeling format for complex datasets (2021) not zbMATH
  2. Lüddecke, Timo; Agostini, Alejandro; Fauth, Michael; Tamosiunaite, Minija; Wörgötter, Florentin: Distributional semantics of objects in visual scenes in comparison to text (2019)
  3. Liu, Yang; Feng, Lin; Liu, Shenglan; Sun, Muxin: Global similarity preserving hashing (2018)
  4. Zhu, Zhihui; Li, Gang; Ding, Jiajun; Li, Qiuwei; He, Xiongxiong: On collaborative compressive sensing systems: the framework, design, and algorithm (2018)
  5. Kim, Minyoung: Efficient histogram dictionary learning for text/image modeling and classification (2017)
  6. Eiter, Thomas; Kaminski, Tobias: Exploiting contextual knowledge for hybrid classification of visual objects (2016)
  7. Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele: The Cityscapes Dataset for Semantic Urban Scene Understanding (2016) arXiv
  8. Borji, Ali: What is a salient object? A dataset and a baseline model for salient object detection (2015)
  9. Le, Tam; Cuturi, Marco: Adaptive Euclidean maps for histograms: generalized Aitchison embeddings (2015)
  10. Bottou, Léon: From machine learning to machine reasoning (2014) ioport
  11. Eslami, S. M. Ali; Heess, Nicolas; Williams, Christopher K. I.; Winn, John: The shape Boltzmann machine: a strong model of object shape (2014)
  12. Gong, Boqing; Grauman, Kristen; Sha, Fei: Learning kernels for unsupervised domain adaptation with applications to visual object recognition (2014)
  13. Kapoor, Ashish; Caicedo, Juan C.; Lischinski, Dani; Kang, Sing Bing: Collaborative personalization of image enhancement (2014) ioport
  14. Li, Li-Jia; Su, Hao; Lim, Yongwhan; Fei-Fei, Li: Object bank: an object-level image representation for high-level visual recognition (2014) ioport
  15. Mesnil, Grégoire; Bordes, Antoine; Weston, Jason; Chechik, Gal; Bengio, Yoshua: Learning semantic representations of objects and their parts (2014)
  16. Patterson, Genevieve; Xu, Chen; Su, Hang; Hays, James: The SUN attribute database: beyond categories for deeper scene understanding (2014) ioport
  17. Vijayanarasimhan, Sudheendra; Grauman, Kristen: Large-scale live active learning: training object detectors with crawled data and crowds (2014) ioport
  18. Payet, Nadia; Todorovic, Sinisa: SLEDGE: sequential labeling of image edges for boundary detection (2013)
  19. Tighe, Joseph; Lazebnik, Svetlana: Superparsing (2013) ioport
  20. Vondrick, Carl; Patterson, Donald; Ramanan, Deva: Efficiently scaling up crowdsourced video annotation (2013) ioport

1 2 3 next