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Results 11 - 20
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768
Automatic discovery of meaningful object parts with latent CRFs
- In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010. Bibliography 192
"... Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a balance between being flexible enough to model such vari-ation and discriminative enough to detect objects in clut-tered, ..."
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Cited by 23 (0 self)
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of interest and carried forward to the multi-label or object part case. Our experiments illus-trate the meaningfulness of the discovered parts and demon-strate state-of-the-art performance of the approach. 1.
Normality-Toward a Meaningful Construct
"... The condition of alienation, of being asleep, of being unconscious, of being out of one’s mind, is the condition of the normal man.-R. D. Laing T HE TERM “NORMAL ” has become, if it has not already been, a liability to the special language of adult psychiatry. Despite the shaky foundation supporting ..."
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that mental health professionals use the term in a way which allows them to assess and label specific behaviors. This usage is unfortunate in that the term “normal ” does not designate a valid construct, nor is there a relevant scientifically meaningful body of psychiatric knowledge from which to Proceed
Discovering personally meaningful places: An interactive clustering approach
- ACM Trans. Inf. Syst
"... The discovery of a person’s meaningful places involves obtaining the physical locations and their labels for a person’s places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obta ..."
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Cited by 25 (1 self)
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The discovery of a person’s meaningful places involves obtaining the physical locations and their labels for a person’s places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries
A statistical model for general contextual object recog. ECCV
, 2004
"... Abstract. We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects. Given a set of images and their associated text (e.g. keywords, captions, descriptions), the objective is to ..."
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Cited by 129 (7 self)
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Abstract. We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects. Given a set of images and their associated text (e.g. keywords, captions, descriptions), the objective
Meaningful results for Information Retrieval in the MEANING project
- In Proc. of the 3rd Global Wordnet Conference
, 2006
"... The goal of the MEANING project (IST-2001-34460) is to develop tools for the automatic acquisition of lexical knowledge that will help Word Sense Disambiguation (WSD). The acquired lexical knowledge from various sources and various languages is stored in the Multilingual Central Repository (MCR) (At ..."
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Cited by 5 (3 self)
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) (Atserias et al 04), which is based on the design of the EuroWordNet database. The MCR holds wordnets in various languages (English, Spanish, Italian, Catalan and Basque), which are interconnected via an Inter-Lingual-Index (ILI). In addition, the MCR holds a number of ontologies and domain labels related
Phrase: A natural, meaningful, unambiguous semantic unit
, 2015
"... Mining semantically meaningful phrases Transform text data from word granularity to phrase granularity Enhance the power and efficiency at manipulating unstructured data using database technology 4Mining Phrases: Why Not Use NLP Methods? Phrase mining was originated from the NLP community Name ..."
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Mining semantically meaningful phrases Transform text data from word granularity to phrase granularity Enhance the power and efficiency at manipulating unstructured data using database technology 4Mining Phrases: Why Not Use NLP Methods? Phrase mining was originated from the NLP community Name
Scaling Politically Meaningful Dimensions Using Texts and Votes
, 2013
"... Item response theory models for roll-call voting data provide political scientists with parsimonious descriptions of political actors ’ relative preferences. However, models using only voting data tend to obscure variation in preferences across different issues due to identification and labeling pro ..."
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Cited by 3 (2 self)
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Item response theory models for roll-call voting data provide political scientists with parsimonious descriptions of political actors ’ relative preferences. However, models using only voting data tend to obscure variation in preferences across different issues due to identification and labeling
Global considerations in hierarchical clustering reveal meaningful patterns
- in data. PloS one. 2008; 3(5):e2247. doi: 10.1371/journal.pone.0002247 PMID: 18493326
"... Background: A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the on ..."
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Cited by 3 (0 self)
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the only method applied. Methodology/Principal Findings: We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing
ESP: Labeling Images with a Computer Game
"... We present a new interactive system: a game that is fun and can be used to create valuable output. When people play the game they help determine the contents of images by providing meaningful labels for them. If the game is played as much as popular online games, we estimate that most images on the ..."
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Cited by 4 (0 self)
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We present a new interactive system: a game that is fun and can be used to create valuable output. When people play the game they help determine the contents of images by providing meaningful labels for them. If the game is played as much as popular online games, we estimate that most images
Learning Spatial Context: Using Stuff to Find Things
"... Abstract. The sliding window approach of detecting rigid objects (such as cars) is predicated on the belief that the object can be identified from the appearance in a small region around the object. Other types of objects of amorphous spatial extent (e.g., trees, sky), however, are more naturally cl ..."
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Cited by 131 (1 self)
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providing an explicit training set with region labels, our method automatically groups regions based on both their appearance and their relationships to the detections in the image. We show that our things and stuff (TAS) context model produces meaningful clusters that are readily interpretable, and helps
Results 11 - 20
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768
