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» Automatic Image Annotation Using Maximum Entropy Model
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ICMCS
2006
IEEE
124views Multimedia» more  ICMCS 2006»
14 years 1 months ago
Content-Free Image Retrieval using Bayesian Product Rule
Content-free image retrieval uses accumulated user feedback records to retrieve images without analyzing image pixels. We present a Bayesian-based algorithm to analyze user feedba...
David Liu, Tsuhan Chen
LREC
2008
131views Education» more  LREC 2008»
13 years 9 months ago
Learning Morphology with Morfette
Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system i...
Grzegorz Chrupala, Georgiana Dinu, Josef van Genab...
MMM
2012
Springer
313views Multimedia» more  MMM 2012»
12 years 3 months ago
Combining Image-Level and Segment-Level Models for Automatic Annotation
Abstract. For the task of assigning labels to an image to summarize its contents, many early attempts use segment-level information and try to determine which parts of the images c...
Daniel Küttel, Matthieu Guillaumin, Vittorio ...
NIPS
2003
13 years 8 months ago
A Model for Learning the Semantics of Pictures
We propose an approach to learning the semantics of images which allows us to automatically annotate an image with keywords and to retrieve images based on text queries. We do thi...
Victor Lavrenko, R. Manmatha, Jiwoon Jeon
MM
2004
ACM
219views Multimedia» more  MM 2004»
14 years 27 days ago
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Automatic image annotation is a promising solution to enable semantic image retrieval via keywords. In this paper, we propose a multi-level approach to annotate the semantics of n...
Jianping Fan, Yuli Gao, Hangzai Luo