SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries

Abstract. In this paper, we propose an acceleration of collapsed variational Bayesian (CVB) inference for latent Dirichlet allocation (LDA) by using Nvidia CUDA compatible devices. While LDA is an efficient Bayesian multi-topic document model, it requires complicated computations for parameter estimation in comparison with other simpler document models, e.g. probabilistic latent semantic indexing, etc. Therefore, we accelerate CVB inference, an efficient deterministic inference method for LDA, with Nvidia CUDA. In the evaluation experiments, we used a set of 50,000 documents and a set of 10,000 images. We could obtain inference results comparable to sequential CVB inference.

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“…Wang [16] [15]. Since our aim is not to propose a new image processing method, we used a simple feature extraction.…”

Section: Datasetsmentioning

confidence: 99%

Accelerating Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation with Nvidia CUDA Compatible Devices

Masada

Hamada

Shibata

et al. 2009

Next-Generation Applied Intelligence

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“…Our experiments are performed on the SIMPLIcity dataset [25]. It is a subset of the COREL database, formed by 10 image categories, each containing 100 images.…”

Section: Datasetmentioning

confidence: 99%

Image Categorization Using ESFS: A New Embedded Feature Selection Method Based on SFS

Fu¹,

Xiao²,

Dellandréa³

et al. 2009

Advanced Concepts for Intelligent Vision Systems

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Abstract. Feature subset selection is an important subject when training classifiers in Machine Learning (ML) problems. Too many input features in a ML problem may lead to the so-called "curse of dimensionality", which describes the fact that the complexity of the classifier parameters adjustment during training increases exponentially with the number of features. Thus, ML algorithms are known to suffer from important decrease of the prediction accuracy when faced with many features that are not necessary. In this paper, we introduce a novel embedded feature selection method, called ESFS, which is inspired from the wrapper method SFS since it relies on the simple principle to add incrementally most relevant features. Its originality concerns the use of mass functions from the evidence theory that allows to merge elegantly the information carried by features, in an embedded way, and so leading to a lower computational cost than original SFS. This approach has successfully been applied to the domain of image categorization and has shown its effectiveness through the comparison with other feature selection methods.

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“…Wang et al [4] have used a 2-step algorithm using subband variances of wavelet coefficients for image retrieval applications. Cheng et al [5] have used M-band wavelet transform based histogram for content based retrieval of aerial images.…”

Section: Introductionmentioning

confidence: 99%

Interactive Image Retrieval with Wavelet Features

Kundu

Chowdhury

Banerjee³

2011

Lecture Notes in Computer Science

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Abstract. This paper presents an iterative Content Based Image Retrival(CBIR) system with Relevance Feedback (RF), in which M-band wavelet features are used as representation of images. The pixels are clustered using Fuzzy C-Means (FCM) clustering algorithm to obtain an image signature and Earth Mover's Distance (EMD) is used as a distance measure. Fuzzy entropy based feature evaluation mechanism is used for automatic computation of revised feature importance and similarity distance at the end of each iteration. The performance of the algorithm is tested on standard large multi-class image databases and compared with MPEG-7 visual features.

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SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries

Cited by 254 publications

References 20 publications

Accelerating Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation with Nvidia CUDA Compatible Devices

Accelerating Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation with Nvidia CUDA Compatible Devices

Image Categorization Using ESFS: A New Embedded Feature Selection Method Based on SFS

Interactive Image Retrieval with Wavelet Features

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