Results 1 - 10
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202
Sun database: Largescale scene recognition from abbey to zoo
- In CVPR
"... Scene categorization is a fundamental problem in com-puter vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object cate-gorization contain hund ..."
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Cited by 306 (37 self)
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Scene categorization is a fundamental problem in com-puter vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object cate-gorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of perfor-mance. We measure human scene classification perfor-mance on the SUN database and compare this with com-putational methods. Additionally, we study a finer-grained scene representation to detect scenes embedded inside of larger scenes. 1.
SuperParsing: Scalable Nonparametric Image Parsing with Superpixels
"... Abstract. This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach requires no training, and it can easily scale to datasets with ten ..."
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Cited by 128 (4 self)
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Abstract. This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach requires no training, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. It works by scene-level matching with global image descriptors, followed by superpixel-level matching with local features and efficient Markov random field (MRF) optimization for incorporating neighborhood context. Our MRF setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). Our system outperforms the state-of-the-art nonparametric method based on SIFT Flow on a dataset of 2,688 images and 33 labels. In addition, we report per-pixel rates on a larger dataset of 15,150 images and 170 labels. To our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem. Key words: scene understanding, image parsing, image segmentation 1
An Empirical Study of Context in Object Detection
"... This paper presents an empirical evaluation of the role of context in a contemporary, challenging object detection task – the PASCAL VOC 2008. Previous experiments with context have mostly been done on home-grown datasets, often with non-standard baselines, making it difficult to isolate the contrib ..."
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Cited by 105 (4 self)
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This paper presents an empirical evaluation of the role of context in a contemporary, challenging object detection task – the PASCAL VOC 2008. Previous experiments with context have mostly been done on home-grown datasets, often with non-standard baselines, making it difficult to isolate the contribution of contextual information. In this work, we present our analysis on a standard dataset, using topperforming local appearance detectors as baseline. We evaluate several different sources of context and ways to utilize it. While we employ many contextual cues that have been used before, we also propose a few novel ones including the use of geographic context and a new approach for using object spatial support. 1.
Unsupervised discovery of mid-level discriminative patches. arXiv:1205.3137 [cs.CV
, 2012
"... Abstract. The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation. The desired patches need to satisfy two requirements: 1) to be representative, they need to occur frequently enough in the visual world; 2) to be d ..."
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Cited by 79 (4 self)
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Abstract. The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation. The desired patches need to satisfy two requirements: 1) to be representative, they need to occur frequently enough in the visual world; 2) to be discriminative, they need to be different enough from the rest of the visual world. The patches could correspond to parts, objects, “visual phrases”, etc. but are not restricted to be any one of them. We pose this as an unsupervised discriminative clustering problem on a huge dataset of image patches. We use an iterative procedure which alternates between clustering and training discriminative classifiers, while applying careful cross-validation at each step to prevent overfitting. The paper experimentally demonstrates the effectiveness of discriminative patches as an unsupervised mid-level visual representation, suggesting that it could be used in place of visual words for many tasks. Furthermore, discriminative patches can also be used in a supervised regime, such as scene classification, where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset. 1
Tour the world: Building a web-scale landmark recognition engine
- in: IEEE Conference on Computer Vision and Pattern Recognition, Electronic Proceedings
, 2009
"... Modeling and recognizing landmarks at world-scale is a useful yet challenging task. There exists no readily available list of worldwide landmarks. Obtaining reliable visual models for each landmark can also pose problems, and efficiency is another challenge for such a large scale system. This paper ..."
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Cited by 78 (1 self)
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Modeling and recognizing landmarks at world-scale is a useful yet challenging task. There exists no readily available list of worldwide landmarks. Obtaining reliable visual models for each landmark can also pose problems, and efficiency is another challenge for such a large scale system. This paper leverages the vast amount of multimedia data on the web, the availability of an Internet image search engine, and advances in object recognition and clustering techniques, to address these issues. First, a comprehensive list of landmarks is mined from two sources: (1) ∼20 million GPS-tagged photos and (2) online tour guide web pages. Candidate images for each landmark are then obtained from photo sharing websites or by querying an image search engine. Second, landmark visual models are built by pruning candidate images using efficient image matching and unsupervised clustering techniques. Finally, the landmarks and their visual models are validated by checking authorship of their member images. The resulting landmark recognition engine incorporates 5312 landmarks from 1259 cities in 144 countries. The experiments demonstrate that the engine can deliver satisfactory recognition performance with high efficiency. 1.
Im2Text: Describing Images Using 1 Million Captioned Photographs
"... We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with ..."
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Cited by 77 (5 self)
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We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning. 1
CENTRIST: A Visual Descriptor for Scene Categorization
- SUBMITTED TO IEEE TRANS. PAMI
, 2009
"... CENTRIST (CENsus TRansform hISTogram), a new visual descriptor for recognizing topological places or scene categories, is introduced in this paper. We show that place and scene recognition, especially for indoor environments, require its visual descriptor to possess properties that are different fro ..."
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Cited by 75 (12 self)
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CENTRIST (CENsus TRansform hISTogram), a new visual descriptor for recognizing topological places or scene categories, is introduced in this paper. We show that place and scene recognition, especially for indoor environments, require its visual descriptor to possess properties that are different from other vision domains (e.g. object recognition). CENTRIST satisfy these properties and suits the place and scene recognition task. It is a holistic representation and has strong generalizability for category recognition. CENTRIST mainly encodes the structural properties within an image and suppresses detailed textural information. Our experiments demonstrate that CENTRIST outperforms the current state-of-the-art in several place and scene recognition datasets, compared with other descriptors such as SIFT and Gist. Besides, it is easy to implement. It has nearly no parameter to tune, and evaluates extremely fast.
Location Recognition using Prioritized Feature Matching
"... Abstract. We present a fast, simple location recognition and image localization method that leverages feature correspondence and geometry estimated from large Internet photo collections. Such recovered structure contains a significant amount of useful information about images and image features that ..."
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Cited by 71 (5 self)
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Abstract. We present a fast, simple location recognition and image localization method that leverages feature correspondence and geometry estimated from large Internet photo collections. Such recovered structure contains a significant amount of useful information about images and image features that is not available when considering images in isolation. For instance, we can predict which views will be the most common, which feature points in a scene are most reliable, and which features in the scene tend to co-occur in the same image. Based on this information, we devise an adaptive, prioritized algorithm for matching a representative set of SIFT features covering a large scene to a query image for efficient localization. Our approach is based on considering features in the scene database, and matching them to query image features, as opposed to more conventional methods that match image features to visual words or database features. We find this approach results in improved performance, due to the richer knowledge of characteristics of the database features compared to query image features. We present experiments on two large city-scale photo collections, showing that our algorithm compares favorably to image retrieval-style approaches to location recognition.
Placing Flickr Photos on a Map
"... In this paper we investigate generic methods for placing photos uploaded to Flickr on the World map. As primary input for our methods we use the textual annotations provided by the users to predict the single most probable location where the image was taken. Central to our approach is a language mod ..."
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Cited by 68 (5 self)
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In this paper we investigate generic methods for placing photos uploaded to Flickr on the World map. As primary input for our methods we use the textual annotations provided by the users to predict the single most probable location where the image was taken. Central to our approach is a language model based entirely on the annotations provided by users. We define extensions to improve over the language model using tag-based smoothing and cell-based smoothing, and leveraging spatial ambiguity. Further we demonstrate how to incorporate GeoNames 1, a large external database of locations. For varying levels of granularity, we are able to place images on a map with at least twice the precision of the state-of-the-art reported in the literature.
Fast Image-Based Localization using Direct 2D-to-3D Matching
- IN: IEEE 13TH INTERNATIONAL CONFERENCE ON COMPUTER VISION
, 2011
"... Recently developed Structure from Motion (SfM) reconstruction approaches enable the creation of large scale 3D models of urban scenes. These compact scene representations can then be used for accurate image-based localization, creating the need for localization approaches that are able to efficientl ..."
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Cited by 63 (8 self)
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Recently developed Structure from Motion (SfM) reconstruction approaches enable the creation of large scale 3D models of urban scenes. These compact scene representations can then be used for accurate image-based localization, creating the need for localization approaches that are able to efficiently handle such large amounts of data. An important bottleneck is the computation of 2D-to-3D correspondences required for pose estimation. Current stateof-the-art approaches use indirect matching techniques to accelerate this search. In this paper we demonstrate that direct 2D-to-3D matching methods have a considerable potential for improving registration performance. We derive a direct matching framework based on visual vocabulary quantization and a prioritized correspondence search. Through extensive experiments, we show that our framework efficiently handles large datasets and outperforms current state-of-the-art methods.
