Random Subwindows for Robust Image Classification

Marée, Raphaël; Geurts, Pierre; Piater, Justus; Wehenkel, Louis

doi:10.1109/cvpr.2005.287

Cited by 159 publications

(132 citation statements)

References 8 publications

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“…The approach presented here for time-series is essentially identical to the work reported in [9] for image classification. Similar ideas could also be exploited to yield generic approaches for the classification of texts or biological sequences.…”

Section: Resultsmentioning

confidence: 94%

Segment and Combine Approach for Non-parametric Time-Series Classification

Geurts

Wehenkel

2005

Knowledge Discovery in Databases: PKDD 2005

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Abstract. This paper presents a novel, generic, scalable, autonomous, and flexible supervised learning algorithm for the classification of multivariate and variable length time series. The essential ingredients of the algorithm are randomization, segmentation of time-series, decision tree ensemble based learning of subseries classifiers, combination of subseries classification by voting, and cross-validation based temporal resolution adaptation. Experiments are carried out with this method on 10 synthetic and real-world datasets. They highlight the good behavior of the algorithm on a large diversity of problems. Our results are also highly competitive with existing approaches from the literature.

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Section: Resultsmentioning

confidence: 94%

Segment and Combine Approach for Non-parametric Time-Series Classification

Geurts

Wehenkel

2005

Knowledge Discovery in Databases: PKDD 2005

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“…Manually acquired images of zebrafish embryos are first pre-processed to standardize images which are then submitted to a phenotypic classification. The supervised learning algorithm used is based on random subwindows extraction in images [15], their description by raw pixel values, and the use of ensembles of extremely randomized trees [10] to classify these subwindows hence images.…”

Section: Related Workmentioning

confidence: 99%

Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods

Stern

Marée

Aceto

et al. 2011

Pattern Recognition in Bioinformatics

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Abstract. In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are going through normal cell cycles, how organisms evolve in different experimental conditions, etc. But, with the large number of images acquired in high-throughput experiments, this manual inspection becomes lengthy, tedious and error-prone. In this paper, we propose to automatically detect specific interest points in microscopy images using machine learning methods with the aim of performing automatic morphometric measurements in the context of Zebrafish studies. We systematically evaluate variants of ensembles of classification and regression trees on four datasets corresponding to different imaging modalities and experimental conditions. Our results show that all variants are effective, with a slight advantage for multiple output methods, which are more robust to parameter choices.

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“…Reasons for using extra-trees in our context are threefold: (1) extra-trees have proven successful for solving some color image classification tasks [21], (2) they form a non-parametric function approximation architecture, which do not require previous knowledge, and (3) they have low bias and variance, as well as good performances in generalization.…”

Section: Silhouettes Classificationmentioning

confidence: 99%

“…We must therefore introduce a meta-rule over the extra-trees for mapping a set C(X) to a class. In this work, we exploit an idea that is similar to that of MARÉE et al, which was used in the context of image classification [21].…”

Section: Classification Of Silhouettesmentioning

confidence: 99%

Robust Analysis of Silhouettes by Morphological Size Distributions

Barnich

Jodogne

Droogenbroeck

2006

Advanced Concepts for Intelligent Vision Systems

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Abstract. We address the topic of real-time analysis and recognition of silhouettes. The method that we propose first produces object features obtained by a new type of morphological operators, which can be seen as an extension of existing granulometric filters, and then insert them into a tailored classification scheme. Intuitively, given a binary segmented image, our operator produces the set of all the largest rectangles that can be wedged inside any connected component of the image. The later are obtained by a standard background subtraction technique and morphological filtering. To classify connected components into one of the known object categories, the rectangles of a connected component are submitted to a machine learning algorithm called EXtremely RAndomized trees (Extra-trees). The machine learning algorithm is fed with a static database of silhouettes that contains both positive and negative instances. The whole process, including image processing and rectangle classification, is carried out in real-time. Finally we evaluate our approach on one of today's hot topic: the detection of human silhouettes. We discuss experimental results and show that our method is stable and computationally effective. Therefore, we assess that algorithms like ours introduce new ways for the detection of human in video sequences.

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Random Subwindows for Robust Image Classification

Abstract: We

Cited by 159 publications

References 8 publications

Segment and Combine Approach for Non-parametric Time-Series Classification

Segment and Combine Approach for Non-parametric Time-Series Classification

Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods

Robust Analysis of Silhouettes by Morphological Size Distributions

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