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52
Image Retrieval via Probabilistic Hypergraph Ranking
"... In this paper, we propose a new transductive learning framework for image retrieval, in which images are taken as vertices in a weighted hypergraph and the task of image search is formulated as the problem of hypergraph ranking. Based on the similarity matrix computed from various feature descriptor ..."
Abstract
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Cited by 24 (0 self)
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In this paper, we propose a new transductive learning framework for image retrieval, in which images are taken as vertices in a weighted hypergraph and the task of image search is formulated as the problem of hypergraph ranking. Based on the similarity matrix computed from various feature
Relative attributes
- In Proceedings of ICCV ’11
, 2011
"... Human-nameable visual “attributes ” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person is ‘smiling ’ or not, a scene is ‘dry ’ or not), and thus fail to capture more general semantic relationships. We propose to ..."
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Cited by 151 (20 self)
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to model relative attributes. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We then build a generative model over
Face alignment via boosted ranking model
- In Proceedings of the European Conference on Computer Vision
, 2008
"... Face alignment seeks to deform a face model to match it with the features of the image of a face by optimizing an appropriate cost function. We propose a new face model that is aligned by maximizing a score function, which we learn from training data, and that we impose to be concave. We show that t ..."
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Cited by 25 (1 self)
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Face alignment seeks to deform a face model to match it with the features of the image of a face by optimizing an appropriate cost function. We propose a new face model that is aligned by maximizing a score function, which we learn from training data, and that we impose to be concave. We show
Scene Classification via Hypergraph-Based Semantic Attributes Subnetworks Identification
"... Abstract. Scene classification is an important issue in computer vision area. However, it is still a challenging problem due to the variability, ambiguity, and scale change that exist commonly in images. In this pa-per, we propose a novel hypergraph-based modeling that considers the higher-order rel ..."
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Abstract. Scene classification is an important issue in computer vision area. However, it is still a challenging problem due to the variability, ambiguity, and scale change that exist commonly in images. In this pa-per, we propose a novel hypergraph-based modeling that considers the higher
Image Retrieval and Ranking via Consistently Reconstructing Multi-attribute Queries
"... Abstract. Image retrieval and ranking based on the multi-attribute queries is beneficial to various real world applications. Traditional methods on this prob-lem often utilize intermediate representations generated by attribute classifiers to describe the images, and then the images in the database ..."
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Abstract. Image retrieval and ranking based on the multi-attribute queries is beneficial to various real world applications. Traditional methods on this prob-lem often utilize intermediate representations generated by attribute classifiers to describe the images, and then the images in the database
Boosting sparse representations for image retrieval
, 2000
"... we developed and implemented a method for creating sparse representations of real images for image retrieval. Feature selection occurs both offline by choosing highly selective features and online via “boosting”. A tree of repeated filtering with simple kernels is used to compute the initial set of ..."
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Cited by 1 (0 self)
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we developed and implemented a method for creating sparse representations of real images for image retrieval. Feature selection occurs both offline by choosing highly selective features and online via “boosting”. A tree of repeated filtering with simple kernels is used to compute the initial set
Distributed Cosegmentation via Submodular Optimization on Anisotropic Diffusion
"... The saliency of regions or objects in an image can be significantly boosted if they recur in multiple images. Leveraging this idea, cosegmentation jointly segments common regions from multiple images. In this paper, we propose CoSand, a distributed cosegmentation approach for a highly variable large ..."
Abstract
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Cited by 47 (7 self)
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The saliency of regions or objects in an image can be significantly boosted if they recur in multiple images. Leveraging this idea, cosegmentation jointly segments common regions from multiple images. In this paper, we propose CoSand, a distributed cosegmentation approach for a highly variable
Learning Semantic Attributes via a Common Latent Space
"... Abstract: Semantic attributes represent an adequate knowledge that can be easily transferred to other domains where lack of information and training samples exist. However, in the classical object recognition case, where training data is abundant, attribute-based recognition usually results in poor ..."
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Cited by 2 (1 self)
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performance compared to methods that used image features directly. We introduce a generic framework that boosts the performance of semantic attributes considerably in traditional classification and knowledge transfer tasks, such as zero-shot learning. It incorporates the discriminative power of the visual
CONTEXT RANKING MACHINE AND ITS APPLICATION TO RIGID LOCALIZATION OF DEFORMABLE OBJECTS
"... In this paper, we exploit the context information embodied in an image to develop a machine learning method called context ranking machine (CRM). Specifically, we leverage two kinds of context information: identity context and metric context. The identity context of an image patch refers to its orig ..."
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an image-based ranking algorithm via boosting and achieves an improved localization accuracy. We performed tests on echocardiogram images to localize heart chambers, and face images for eye band localization.
Results 1 - 10
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