We are analyzing https://link.springer.com/article/10.1007/s11263-007-0090-8.

Title:
LabelMe: A Database and Web-Based Tool for Image Annotation | International Journal of Computer Vision
Description:
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.
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Keywords {🔍}

object, computer, recognition, vision, article, international, learning, ieee, cvpr, torralba, detection, conference, google, scholar, journal, image, russell, freeman, images, annotation, dataset, objects, visual, information, database, tool, pattern, zisserman, privacy, cookies, content, data, search, murphy, categories, discover, categorization, springer, perona, publish, research, webbased, access, chapter, machine, transactions, intelligence, technical, report, downloaded,

Topics {✒️}

uk/research/vssp/demos/colour/soil47/ month download article/chapter semi-automatic visual learning generative-model based vision /vision/cambridge/recognition/default org/challenges/voc/voc2006/ discover object parts annotation object detection privacy choices/manage cookies related subjects open mind initiative visual object recognition interleaved object categorization combined object categorization image annotation published easy image annotation web-based tool generic object recognition full article pdf improving object detection fast object detection multiclass object detection rapid object detection electronic lexical database boundary-fragment-model pascal challenges workshop european economic area minimal user supervision /image_datasets/caltech101/caltech101 special issue consistent line clusters describing visual scenes transformed dirichlet processes learning hierarchical models learning linear models article international journal human image interpretation conditions privacy policy ground truth labels part-based representation contour based methods ieee intelligent systems learning object categories 10th international conference annotation tool implicit shape model artificial intelligence laboratory accepting optional cookies computer vision aims carnegie mellon university

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         description: 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.
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         Pattern Recognition
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