Book ChapterDOI
Modeling hidden topics with dual local consistency for image analysis
Peng Li,Jian Cheng,Hanqing Lu +2 more
- pp 648-659
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TLDR
A novel topic model is presented, which can learn an effective and robust mid-level representation in the latent semantic space for image analysis and understanding and considers both the local image structure and local word consistency simultaneously when estimating the probabilistic topic distributions.Citations
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Book ChapterDOI
Local Structure Preserving Based Subspace Analysis Methods and Applications
Jian Cheng,Hanqing Lu +1 more
TL;DR: This chapter proposes two novel subspace analysis methods for face recognition and image clustering tasks, and proposes a novel probabilistic topic model forimage clustering task, named Dual Local Consistency Probabilistic Latent Semantic Analysis (DLC-PLSA).
Journal ArticleDOI
Learning latent semantic model with visual consistency for image analysis
TL;DR: This paper considers simultaneously the topic consistency and word consistency in semantic space to adapt the traditional PLSA model to the visual content analysis tasks.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.