Maximal Similarity Embedding

Feng, Lin; Liu, Shenglan; Wu, Zhenyu; Jin, Bo

doi:10.1016/j.neucom.2012.07.018

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…This method has shown superior performance for large-scale image retrieval. Scatter balance metric learning (SBML) is introduced to measure the similarity among image features based on angle linear discriminant embedding (ALDE) [54]. As a global dimensionality reduction method, ALDE aims to find transformation matrix U to obtain low-dimensional projection y = U T x through scatter balance.…”

Section: Resultsmentioning

confidence: 99%

Perceptual uniform descriptor and ranking on manifold for image retrieval

Liu

Feng

et al. 2018

Information Sciences

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Section: Resultsmentioning

confidence: 99%

Perceptual uniform descriptor and ranking on manifold for image retrieval

Liu

Feng

et al. 2018

Information Sciences

Self Cite

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“…To evaluate proposed algorithms CSML and KCSML, in this section, we perform several image classification experiments on diverse databases and compare them with another popular related work. These comparable methods include principal component analysis (PCA), random subspace two-dimensional PCA (RS-2DPCA), linear discriminant analysis (LDA), local preserving projection (LPP), marginal fisher analysis (MFA) [ 27 ], correlation embedding analysis (CEA), correlation discriminant analysis (CDA), improved similarity measure-based graph embedding (ISM-GE) [ 46 ], and maximal similarity embedding (MSE) [ 47 ]. PCA is taken as a baseline method.…”

Section: Methodsmentioning

confidence: 99%

Similarity Measure Learning in Closed-Form Solution for Image Classification

Chen

Tang

Chen

et al. 2014

The Scientific World Journal

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Adopting a measure is essential in many multimedia applications. Recently, distance learning is becoming an active research problem. In fact, the distance is the natural measure for dissimilarity. Generally, a pairwise relationship between two objects in learning tasks includes two aspects: similarity and dissimilarity. The similarity measure provides different information for pairwise relationships. However, similarity learning has been paid less attention in learning problems. In this work, firstly, we propose a general framework for similarity measure learning (SML). Additionally, we define a generalized type of correlation as a similarity measure. By a set of parameters, generalized correlation provides flexibility for learning tasks. Based on this similarity measure, we present a specific algorithm under the SML framework, called correlation similarity measure learning (CSML), to learn a parameterized similarity measure over input space. A nonlinear extension version of CSML, kernel CSML, is also proposed. Particularly, we give a closed-form solution avoiding iterative search for a local optimal solution in the high-dimensional space as the previous work did. Finally, classification experiments have been performed on face databases and a handwritten digits database to demonstrate the efficiency and reliability of CSML and KCSML.

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“…where H 4 ⊆ X s * t2 . Dimensionality reduction methods can fall into many categories, such as linear methods principal component analysis (PCA) [52], maximal similarity embedding (MSE) [22], and nonlinear ones isometric mapping (ISOMAP) [53], Locally Linear Embedding (LLE) [54], local tangent space alignment (LTSA) [55], t-distributed stochastic neighbor embedding (T-SNE) [56], symmetric positive definite (SPD) manifold dimension reduction [57], path based Isomap [58] and so on. In our proposed PVIL, visualization (Eq.…”

Section: Perceptual Visual Interactive Learning (Pvil) Frameworkmentioning

confidence: 99%

“…To improve the performance of KNN, some extended versions (e.g. L 1 -norm based KNN, extended nearest neighbor (ENN) and cosine metric based KNN [21] [22]) are proposed according to KNN framework. However, lazy learning methods (including KNN and the extended versions) strong relate to the distribution and the number of training samples (KNNbased methods are always difficult to apply to large-scale data sets and are quite time consuming.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Visual Interactive Learning

Liu¹,

Liu²,

Liu³

et al. 2018

Preprint

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Supervised learning methods are widely used in machine learning. However, the lack of labels in existing data limits the application of these technologies. Visual interactive learning (VIL) compared with computers can avoid semantic gap, and solve the labeling problem of small label quantity (SLQ) samples in a groundbreaking way. In order to fully understand the importance of VIL to the interaction process, we re-summarize the interactive learning related algorithms (e.g. clustering, classification, retrieval etc.) from the perspective of VIL. Note that, perception and cognition are two main visual processes of VIL. On this basis, we propose a perceptual visual interactive learning (PVIL) framework, which adopts gestalt principle to design interaction strategy and multi-dimensionality reduction (MDR) to optimize the process of visualization. The advantage of PVIL framework is that it combines computer's sensitivity of detailed features and human's overall understanding of global tasks. Experimental results validate that the framework is superior to traditional computer labeling methods (such as label propagation) in both accuracy and efficiency, which achieves significant classification results on dense distribution and sparse classes dataset.

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Maximal Similarity Embedding

Cited by 9 publications

References 30 publications

Perceptual uniform descriptor and ranking on manifold for image retrieval

Perceptual uniform descriptor and ranking on manifold for image retrieval

Similarity Measure Learning in Closed-Form Solution for Image Classification

Perceptual Visual Interactive Learning

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