Objects in Context

Rabinovich, Andrew; Vedaldi, Andrea; Galleguillos, Carolina; Wiewiora, Eric; Belongie, Serge

doi:10.1109/iccv.2007.4408986

Cited by 539 publications

(469 citation statements)

References 17 publications

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“…Instead, like [3][4][5], we choose to assign labels to superpixels, or regions produced by bottom-up segmentation. This not only reduces the complexity of the problem, but also gives better spatial support for aggregating features that could belong to a single object than, say, fixed-size square patches centered on every pixel in the image.…”

Section: Superpixel Featuresmentioning

confidence: 99%

“…Many state-of-the-art approaches encode such constraints with the help of conditional random field (CRF) models [1,6,4]. However, CRFs tend to be very costly both in terms of learning and inference.…”

Section: Contextual Inferencementioning

confidence: 99%

“…The current consensus among recognition researchers is that image parsing requires context (see, e.g., [3,4,9,10]). However, learning and inference with most existing contextual models is slow and non-exact.…”

Section: Introductionmentioning

confidence: 99%

“…The literature contains diverse proposed image parsing methods, including ones that estimate labels pixel by pixel [1,2], ones that aggregate features over segmentation regions [3][4][5][6], and ones that predict object bounding boxes [7][8][9][10]. Most of these methods operate with a few pre-defined classes and require a generative or discriminative model to be trained in advance for each class (and sometimes even for each training exemplar [5]).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SuperParsing: Scalable Nonparametric Image Parsing with Superpixels

Tighe

Lazebnik

2010

Computer Vision – ECCV 2010

428

621

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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.

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Section: Superpixel Featuresmentioning

confidence: 99%

Section: Contextual Inferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SuperParsing: Scalable Nonparametric Image Parsing with Superpixels

Tighe

Lazebnik

2010

Computer Vision – ECCV 2010

428

621

View full text Add to dashboard Cite

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“…This is a big conceptual difference with respect to approaches like [14,3]. It is also different from the CRF framework of [20], where pairwise cooccurrence frequencies are modeled.…”

Section: Introductionmentioning

confidence: 99%

Scene Discovery by Matrix Factorization

Loeff

Farhadi

2008

Lecture Notes in Computer Science

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Abstract. What constitutes a scene? Defining a meaningful vocabulary for scene discovery is a challenging problem that has important consequences for object recognition. We consider scenes to depict correlated objects and present visual similarity. We introduce a max-margin factorization model that finds a low dimensional subspace with high discriminative power for correlated annotations. We postulate this space should allow us to discover a large number of scenes in unsupervised data; we show scene discrimination results on par with supervised approaches. This model also produces state of the art word prediction results including good annotation completion.

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