Results 1 - 10 of 385

Mining Semantic Affordances of Visual Object Categories

by Yu-wei Chao, Zhan Wang, Rada Mihalcea, Jia Deng
"... Affordances are fundamental attributes of objects. Affor-dances reveal the functionalities of objects and the possible actions that can be performed on them. Understanding af-fordances is crucial for recognizing human activities in vi-sual data and for robots to interact with the world. In this pape ..."
Abstract - Add to MetaCart
. In this paper we introduce the new problem of mining the knowl-edge of semantic affordance: given an object, determining whether an action can be performed on it. This is equiv-alent to connecting verb nodes and noun nodes in Word-Net, or filling an affordance matrix encoding the plausibility of each action-object

Visual categorization with bags of keypoints

by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cédric Bray - In Workshop on Statistical Learning in Computer Vision, ECCV , 2004
"... Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of im ..."
Abstract - Cited by 1005 (14 self) - Add to MetaCart
Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors

Describing objects by their attributes

by Ali Farhadi, Ian Endres, Derek Hoiem, David Forsyth - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR , 2009
"... We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown” ..."
Abstract - Cited by 347 (17 self) - Add to MetaCart
”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a

Discovering objects and their location in images

by Josef Sivic, Bryan C. Russell, Alexei A. Efros, Andrew Zisserman, William T. Freeman - In ICCV , 2005
"... We seek to discover the object categories depicted in a set of unlabelled images. We achieve this using a model developed in the statistical text literature: probabilistic Latent Semantic Analysis (pLSA). In text analysis this is used to discover topics in a corpus using the bag-of-words document re ..."
Abstract - Cited by 272 (9 self) - Add to MetaCart
We seek to discover the object categories depicted in a set of unlabelled images. We achieve this using a model developed in the statistical text literature: probabilistic Latent Semantic Analysis (pLSA). In text analysis this is used to discover topics in a corpus using the bag-of-words document

Category specific semantic impairments

by Elizabeth K. Warrington, T. Shallice - Brain , 1984
"... We report a quantitative investigation of the visual identification and auditory comprehension deficits of 4 patients who had made a partial recovery from herpes simplex encephalitis. Clinical observations had suggested the selective impairment and selective preservation of certain categories of vis ..."
Abstract - Cited by 168 (2 self) - Add to MetaCart
We report a quantitative investigation of the visual identification and auditory comprehension deficits of 4 patients who had made a partial recovery from herpes simplex encephalitis. Clinical observations had suggested the selective impairment and selective preservation of certain categories

Discovering object categories in image collections

by Josef Sivic, Bryan C. Russell, Alexei A. Efros, Andrew Zisserman, William T. Freeman , 2004
"... Given a set of images containing multiple object categories, we seek to discover those categories and their image locations without supervision. We achieve this using generative models from the statistical text literature: probabilistic Latent Semantic Analysis (pLSA), and Latent Dirichlet Allocatio ..."
Abstract - Cited by 197 (12 self) - Add to MetaCart
Given a set of images containing multiple object categories, we seek to discover those categories and their image locations without supervision. We achieve this using generative models from the statistical text literature: probabilistic Latent Semantic Analysis (pLSA), and Latent Dirichlet

Efficient Object Category Recognition Using

by Lorenzo Torresani, Martin Szummer, Andrew Fitzgibbon
"... Abstract. We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories a ..."
Abstract - Cited by 122 (9 self) - Add to MetaCart
to represent visual classes unrelated to the constituent categories ’ semantic meanings. The advantage of this descriptor is that it allows object-category queries to be made against image databases using efficient classifiers (efficient at test time) such as linear support vector machines, and allows

Recognizing Realistic Actions from Videos “in the Wild”

by Jingen Liu, Jiebo Luo, Mubarak Shah
"... In this paper, we present a systematic framework for recognizing realistic actions from videos “in the wild. ” Such unconstrained videos are abundant in personal collections as well as on the web. Recognizing action from such videos has not been addressed extensively, primarily due to the tremendous ..."
Abstract - Cited by 227 (13 self) - Add to MetaCart
visual vocabularies, a divisive information-theoretic algorithm is employed to group semantically related features. Finally, AdaBoost is chosen to integrate all the heterogeneous yet complementary features for recognition. We have tested the framework on the KTH dataset and our own dataset consisting

Semantic hierarchies for visual object recognition

by Marcin Marszałek, Cordelia Schmid - In Proc. IEEE Conf. Computer Vision and Pattern Recognition , 2007
"... In this paper we propose to use lexical semantic networks to extend the state-of-the-art object recognition techniques. We use the semantics of image labels to integrate prior knowledge about inter-class relationships into the visual appearance learning. We show how to build and train a semantic hie ..."
Abstract - Cited by 79 (0 self) - Add to MetaCart
In this paper we propose to use lexical semantic networks to extend the state-of-the-art object recognition techniques. We use the semantics of image labels to integrate prior knowledge about inter-class relationships into the visual appearance learning. We show how to build and train a semantic

Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature

by Hans-michael Müller, Eimear E. Kenny, Paul W. Sternberg - PLoS Biol , 2004
"... We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine. Textpresso’s two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation ..."
Abstract - Cited by 208 (14 self) - Add to MetaCart
of categories of terms for which a database of articles and individual sentences can be searched. The categories are classes of biological concepts (e.g., gene, allele, cell or cell group, phenotype, etc.) and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e
Results 1 - 10 of 385