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Scene categorization using topic model based hierarchical conditional random fields

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TLDR
A novel hierarchical framework for scene categorization is proposed using Conditional Random Fields in a hierarchical setting for discovering the global context of latent topics extracted by Latent Dirichlet Allocation.
Abstract
We propose a novel hierarchical framework for scene categorization. The scene representation is defined by latent topics extracted by Latent Dirichlet Allocation. The interaction of these topics across scene categories is learned by probabilistic graphical modelling. We use Conditional Random Fields in a hierarchical setting for discovering the global context of these topics. The learned random fields are further used for categorization of a new scene. The experimental results of the proposed framework is presented on standard datasets and on image collection obtained from the internet.

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A hierarchical retrieval approach for automatically generating assembly instructions

TL;DR: In this paper , a hierarchical retrieval approach for automatic generation of assembly instructions based on previously used technical instruction cards is proposed, which significantly improves the quality of new assembly instructions and the speed of generation.
References
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Proceedings ArticleDOI

A Bayesian hierarchical model for learning natural scene categories

TL;DR: This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning.
Proceedings ArticleDOI

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Proceedings ArticleDOI

Unsupervised discovery of visual object class hierarchies

TL;DR: This work proposes to group visual objects using a multi-layer hierarchy tree that is based on common visual elements by adapting to the visual domain the generative hierarchical latent Dirichlet allocation (hLDA) model previously used for unsupervised discovery of topic hierarchies in text.
Journal ArticleDOI

Learning Conditional Random Fields for Classification of Hyperspectral Images

TL;DR: An efficient local method to train the conditional random field (CRF) and a strategy to combine the independently trained models to obtain final CRF model that is competitive with the most recent results in hyperspectral image classification.
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