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Face recognition using Laplacianfaces
TLDR
Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. Expand
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
TLDR
A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability. Expand
Neighborhood preserving embedding
TLDR
This paper proposes a novel subspace learning algorithm called neighborhood preserving embedding (NPE), which aims at preserving the local neighborhood structure on the data manifold and is less sensitive to outliers than principal component analysis (PCA). Expand
Contrast-based image attention analysis by using fuzzy growing
TLDR
A feasible and fast approach to attention area detection in images based on contrast analysis is proposed and a practicable framework for image attention analysis is presented, which provides three-level attention analysis, i.e., attended view, attended areas and attended points. Expand
Learning spatially localized, parts-based representation
TLDR
A novel method, called local non-negative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual patterns, which gives a set of bases which not only allows a non-subtractive representation of images but also manifests localized features. Expand
Orthogonal Laplacianfaces for Face Recognition
TLDR
An appearance-based face recognition method, called orthogonal Laplacianface, based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. Expand
Automatic partitioning of full-motion video
TLDR
A twin-comparison approach has been developed to solve the problem of detecting transitions implemented by special effects, and a motion analysis algorithm is applied to determine whether an actual transition has occurred. Expand
Automatic mood detection and tracking of music audio signals
TLDR
A hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures, and has the advantage of emphasizing the most suitable features in different detection tasks. Expand
Manifold-ranking based image retrieval
TLDR
MRBIR first makes use of a manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly, which is different from traditional similarity metrics based on pair-wise distance. Expand
Tag ranking
TLDR
This paper proposes a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content, and applies tag ranking into three applications: tag-based image search, tag recommendation, and group recommendation. Expand
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