Learning effective color features for content based image retrieval in dermatology

Bunte, Kerstin; Biehl, Michael; Jonkman, Marcel F.; Petkov, Nicolai

doi:10.1016/j.patcog.2010.10.024

Cited by 64 publications

(39 citation statements)

References 30 publications

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“…On the extracted features they perform Euclidean, Manhattan and Chebychev distances to find the relevant images from image database. In the same way Kerstin Bunte et.al [8] …”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 70%

Correlated Networks for Content Based Image Retrieval

Irtaza¹,

Jaffar²,

Aleisa³

2013

IJCIS

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Efficient CBIR systems are based on three things, (1) how they represent the repository images in the form of signature; (2) how they measure the similarity of the database images with query image, (3) how they retrieve the semantically similar images in response of a query image. The paper is focusing on these three things. For signature development, curvelet transform, wavelet packets, and Gabor filters based signature development is introduced. For measuring the similarity, Pearson correlation is used as a distance measure; and for retrieving the semantically similar images in response of query images, Neural Network based architecture for content based image retrieval is presented. These things ensure the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better than all comparative systems.

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“…On the extracted features they perform Euclidean, Manhattan and Chebychev distances to find the relevant images from image database. In the same way Kerstin Bunte et.al [8] …”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 70%

Correlated Networks for Content Based Image Retrieval

Irtaza¹,

Jaffar²,

Aleisa³

2013

IJCIS

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show abstract

“…If traditional machine learning is concerned with features at all, this is typically limited to the selection of predefined features or using them to derive 'new' features as (linear) combinations of the original ones. Examples are principle component analysis and generalized matrix learning vector quantization [6]. Traditional machine learning is typically not concerned with the question of how the features are defined.…”

Section: Discussionmentioning

confidence: 99%

A Shape Descriptor Based on Trainable COSFIRE Filters for the Recognition of Handwritten Digits

Azzopardi

Petkov

2013

Computer Analysis of Images and Patterns

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Abstract. The recognition of handwritten digits is an application which has been used as a benchmark for comparing shape recognition methods. We train COSFIRE filters to be selective for different parts of handwritten digits. In analogy with the neurophysiological concept of population coding we use the responses of multiple COSFIRE filters as a shape descriptor of a handwritten digit. We demonstrate the effectiveness of the proposed approach on two data sets of handwritten digits: Western Arabic (MNIST) and Farsi for which we achieve high recognition rates of 99.52% and 99.33%, respectively. COSFIRE filters are conceptually simple, easy to implement and they are versatile trainable feature detectors. The shape descriptor that we propose is highly effective to the automatic recognition of handwritten digits.

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“…The idea of determining a discriminative transformation directly within the KNN classification scheme has been put forward in Weinberger, Blitzer, and Saul (2006), there without considering dimension reduction. A more detailed comparison of Large Margin Nearest Neighbor (LMNN) with LiRaM LVQ is given in Bunte, Biehl, Jonkman, and Petkov (2011).…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…These algorithms have been employed successfully in a variety of scientific and commercial applications, including image analysis, bioinformatics, robotics, etc. (Biehl, Ghosh, & Hammer, 2007;Bojer, Hammer, Schunk, & von Toschanowitz, 2001;Bunte, Biehl, Petkov, & Jonkman, 2009;Bunte, Hammer, Schneider, & Biehl, 2009;Bunte, Hammer, Wismüller, & Biehl, 2010a;Hammer, Strickert, & Villmann, 2005a;Hammer & Villmann, 2002;Villmann, Merenyi, & Hammer, 2003). The method is easy to implement and its complexity is controlled by the user in a straightforward way.…”

Section: Introductionmentioning

confidence: 99%

Limited Rank Matrix Learning, discriminative dimension reduction and visualization

et al. 2012

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a b s t r a c tWe present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently.In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two-or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method.

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Learning effective color features for content based image retrieval in dermatology

Cited by 64 publications

References 30 publications

Correlated Networks for Content Based Image Retrieval

Correlated Networks for Content Based Image Retrieval

A Shape Descriptor Based on Trainable COSFIRE Filters for the Recognition of Handwritten Digits

Limited Rank Matrix Learning, discriminative dimension reduction and visualization

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