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LabelMe: A Database and Web-Based Tool for Image Annotation
, 2008
"... 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 sha ..."
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Cited by 678 (46 self)
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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.
80 million tiny images: a large dataset for non-parametric object and scene recognition
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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Building rome in a day.
- In Proc. Int. Conf. on Computer Vision.
, 2009
"... We present a system that can reconstruct 3D geometry from large, unorganized collections of photographs such as those found by searching for a given city (e.g., Rome) on Internet photo-sharing sites. Our system is built on a set of new, distributed computer vision algorithms for image matching and ..."
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Cited by 285 (30 self)
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We present a system that can reconstruct 3D geometry from large, unorganized collections of photographs such as those found by searching for a given city (e.g., Rome) on Internet photo-sharing sites. Our system is built on a set of new, distributed computer vision algorithms for image matching and 3D reconstruction, designed to maximize parallelism at each stage of the pipeline and to scale gracefully with both the size of the problem and the amount of available computation. Our experimental results demonstrate that it is now possible to reconstruct city-scale image collections with more than a hundred thousand images in less than a day. intRoDuction Amateur photography was once largely a personal endeavor. Traditionally, a photographer would capture a moment on film and share it with a small number of friends and family members, perhaps storing a few hundred of them in a shoebox. The advent of digital photography, and the recent growth of photo-sharing Web sites such as Flickr.com, have brought about a seismic change in photography and the use of photo collections. Today, a photograph shared online can potentially be seen by millions of people. As a result, we now have access to a vast, ever-growing collection of photographs the world over capturing its cities and landmarks innumerable times. For instance, a search for the term "Rome" on Flickr returns nearly 3 million photographs. This collection represents an increasingly complete photographic record of the city, capturing every popular site, façade, interior, fountain, sculpture, painting, and café. Virtually anything that people find interesting in Rome has been captured from thousands of viewpoints and under myriad illumination and weather conditions. For example, the Trevi Fountain appears in over 50,000 of these photographs. How much of the city of Rome can be reconstructed in 3D from this photo collection? In principle, the photos of Rome on Flickr represent an ideal data set for 3D modeling research, as they capture the highlights of the city in exquisite detail and from a broad range of viewpoints. However, extracting high quality 3D models from such a collection is challenging for several reasons. First, the photos are unstructured-they are taken in no particular order and we have no control over the distribution of camera viewpoints. Second, they are uncalibrated-the photos are taken by thousands of different photographers and we know very little about the camera settings. Third, the scale of the problem is
Modeling the World from Internet Photo Collections
- INT J COMPUT VIS
, 2007
"... There are billions of photographs on the Internet, comprising the largest and most diverse photo collection ever assembled. How can computer vision researchers exploit this imagery? This paper explores this question from the standpoint of 3D scene modeling and visualization. We present structure-fro ..."
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Cited by 267 (6 self)
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There are billions of photographs on the Internet, comprising the largest and most diverse photo collection ever assembled. How can computer vision researchers exploit this imagery? This paper explores this question from the standpoint of 3D scene modeling and visualization. We present structure-from-motion and image-based rendering algorithms that operate on hundreds of images downloaded as a result of keyword-based image search queries like “Notre Dame ” or “Trevi Fountain.” This approach, which we call Photo Tourism, has enabled reconstructions of numerous well-known world sites. This paper presents these algorithms and results as a first step towards 3D modeling of the world’s well-photographed sites, cities, and landscapes from Internet imagery, and discusses key open problems and challenges for the research community.
Scene completion using millions of photographs
- ACM Transactions on Graphics (SIGGRAPH
, 2007
"... Figure 1: Given an input image with a missing region, we use matching scenes from a large collection of photographs to complete the image. What can you do with a million images? In this paper we present a new image completion algorithm powered by a huge database of photographs gathered from the Web. ..."
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Cited by 250 (12 self)
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Figure 1: Given an input image with a missing region, we use matching scenes from a large collection of photographs to complete the image. What can you do with a million images? In this paper we present a new image completion algorithm powered by a huge database of photographs gathered from the Web. The algorithm patches up holes in images by finding similar image regions in the database that are not only seamless but also semantically valid. Our chief insight is that while the space of images is effectively infinite, the space of semantically differentiable scenes is actually not that large. For many image completion tasks we are able to find similar scenes which contain image fragments that will convincingly complete the image. Our algorithm is entirely data-driven, requiring no annotations or labelling by the user. Unlike existing image completion methods, our algorithm can generate a diverse set of results for each input image and we allow users to select among them. We demonstrate the superiority of our algorithm over existing image completion approaches.
Total recall: Automatic query expansion with a generative feature model for object retrieval.
- In ICCV,
, 2007
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Im2gps: estimating geographic information from a single image
- in IEEE Conference on Computer Vision and Pattern Recognition
, 2008
"... Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally – on the scale of the entire planet ..."
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Cited by 202 (13 self)
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Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally – on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we will leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earth’s surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.
Multi-View Stereo for Community Photo Collections
"... We present a multi-view stereo algorithm that addresses the extreme changes in lighting, scale, clutter, and other effects in large online community photo collections. Our idea is to intelligently choose images to match, both at a per-view and per-pixel level. We show that such adaptive view selecti ..."
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Cited by 185 (23 self)
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We present a multi-view stereo algorithm that addresses the extreme changes in lighting, scale, clutter, and other effects in large online community photo collections. Our idea is to intelligently choose images to match, both at a per-view and per-pixel level. We show that such adaptive view selection enables robust performance even with dramatic appearance variability. The stereo matching technique takes as input sparse 3D points reconstructed from structure-from-motion methods and iteratively grows surfaces from these points. Optimizing for surface normals within a photoconsistency measure significantly improves the matching results. While the focus of our approach is to estimate high-quality depth maps, we also show examples of merging the resulting depth maps into compelling scene reconstructions. We demonstrate our algorithm on standard multi-view stereo datasets and on casually acquired photo collections of famous scenes gathered from the Internet. 1
Small codes and large image databases for recognition
"... The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. These include object recognition [2], computer graphics [11, 27], personal photo collections, online image search tools. In ..."
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Cited by 185 (7 self)
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The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. These include object recognition [2], computer graphics [11, 27], personal photo collections, online image search tools. In this paper, our goal is to develop efficient image search and scene matching techniques that are not only fast, but also require very little memory, enabling their use on standard hardware or even on handheld devices. Our approach uses recently developed machine learning techniques to convert the Gist descriptor (a real valued vector that describes orientation energies at different scales and orientations within an image) to a compact binary code, with a few hundred bits per image. Using our scheme, it
