Features for image retrieval: an experimental comparison

Deselaers, Thomas; Keysers, Daniel; Ney, Hermann

doi:10.1007/s10791-007-9039-3

Cited by 494 publications

(306 citation statements)

References 74 publications

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“…Second, we plan to use other algorithms for feature extraction from images (e.g. Scale-invariant feature transform, SIFT) that were previously successfully used in image annotation [4]. Another line of further work is extensions of the machile learning algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…Instead, it is expected that exploiting the hierarchy will lead to large improvements. Automatic image classification relies on numerical features that are computed from the pixel values [4]. In our approach we use edge histogram descriptor to represents the spatial distribution of five types of edges (four directional edges and one non-directional, see Fig.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical annotation of medical images

Dimitrovski

Kocev

Loskovska³

et al. 2011

Pattern Recognition

144

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In this paper, we describe an approach for the automatic medical annotation task of the 2008 CLEF cross-language image retrieval campaign (ImageCLEF). The data comprise 12076 fully annotated images according to the IRMA code. This work is focused on the process of feature extraction from images and hierarchical multi-label classification. To extract features from the images we used a technique called: local distribution of edges. With this techniques each image was described with 80 variables. The goal of the classification task was to classify an image according to the IRMA code. The IRMA code is organized hierarchically. Hence, as classifer we selected an extension of the predictive clustering trees (PCTs) that is able to handle this type of data. Further more, we constructed ensembles (Bagging and Random Forests) that use PCTs as base classifiers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical annotation of medical images

Dimitrovski

Kocev

Loskovska³

et al. 2011

Pattern Recognition

144

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“…The experimental descriptors that come from general features [6] and lire (An Content Based Image Retrieval Library ) include following kinds :…”

Section: Image Similarity Computing With Combined Featuresmentioning

confidence: 99%

A Flexible Hierarchical Classification Algorithm for Content Based Image Retrieval

Li¹,

Jiang-feng²,

Zhang³

2013

IJFCC

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Abstract-The goal of paper is to describe a flexible hierarchical classification algorithm and a new image similarity computing model based on mixing several image features for promoting the performance and efficiency of speed for content-based image retrieval. With an experimental comparison of a large number of different representative point selection approach, we are trying to seek for a method of uniform division of image space, eventually design a novel approach enlightening by high-dimensional indexing and social networking, that introduces the directivity to image classification that is used to explain the convergence of images to edge points of the high-dimension feature space in this paper. Meanwhile we find the laws of parameter setting of this algorithm through experiments and these laws acquires satisfied effects in different dataset. In addition to that algorithm, we also find some features assembling with reasonable formula to represent images better in color, texture and shape. Experimental results based on a database of about 50,000 person images demonstrate improved performance, as compare with other combinations in our descriptor set consisting of several general features mentioned below.

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“…wavelet transforms), some are based on various descriptors. A variety of descriptors (see [1] for a review) has been proposed in the literature. Local descriptors (e.g.…”

Section: Similarity In Image Processingmentioning

confidence: 99%

Sparse Multiscale Patches for Image Processing

Piro

Anthoine

Debreuve

et al. 2009

Emerging Trends in Visual Computing

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Abstract. This paper presents a framework to define an objective measure of the similarity (or dissimilarity) between two images for image processing. The problem is twofold: 1) define a set of features that capture the information contained in the image relevant for the given task and 2) define a similarity measure in this feature space. In this paper, we propose a feature space as well as a statistical measure on this space. Our feature space is based on a global descriptor of the image in a multiscale transformed domain. After decomposition into a Laplacian pyramid, the coefficients are arranged in intrascale/interscale/interchannel patches which reflect the dependencies of neighboring coefficients in presence of specific structures or textures. At each scale, the probability density function (pdf) of these patches is used as a descriptor of the relevant information. Because of the sparsity of the multiscale transform, the most significant patches, called Sparse Multiscale Patches (SMP), describe efficiently these pdfs. We propose a statistical measure (the Kullback-Leibler divergence) based on the comparison of these probability density functions. Interestingly, this measure is estimated via the nonparametric, k-th nearest neighbor framework without explicitly building the pdfs. This framework is applied to a query-by-example image retrieval method. Experiments on two publicly available databases showed the potential of our SMP approach for this task. In particular, it performed comparably to a SIFT -based retrieval method and two versions of a fuzzy segmentation-based method (the UFM and CLUE methods), and it exhibited some robustness to different geometric and radiometric deformations of the images.

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Features for image retrieval: an experimental comparison

Cited by 494 publications

References 74 publications

Hierarchical annotation of medical images

Hierarchical annotation of medical images

A Flexible Hierarchical Classification Algorithm for Content Based Image Retrieval

Sparse Multiscale Patches for Image Processing

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