Some Results Using Different Approaches to Merge Visual and Text-Based Features in CLEF’08 Photo Collection

Garcı́a-Serrano, Ana; Benavent, Xaro; Granados, Ruben; Goñi-Menoyo, José Miguel

doi:10.1007/978-3-642-04447-2_69

Cited by 3 publications

(1 citation statement)

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“…Euclidean distance is probably the most popular distance metrics. However, in classification problems especially in TC problem, Mahalanobis distance is the most effective ranging standard [16]. The definition of Mahalanobis distance as follows…”

Section: Dimension Reduction Based On Ldamentioning

confidence: 99%

LDA boost classification: boosting by topics

Qiao

Cao

et al. 2012

EURASIP J. Adv. Signal Process.

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AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

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Section: Dimension Reduction Based On Ldamentioning

confidence: 99%