Correspondence analysis applied to textural features recognition

Trujillo, Maite; Sadki, Mustapha

doi:10.1109/iai.2004.1300957

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Zamir and Gabriel [36] used CA to analyze time series on science doctorates in the USA, and Koutsoupias [14] applied CA for the examination of access trends on a web site (based on the web log data). Trujillo and Sadki [33] used CA to reduce the number of texture features, thereby minimizing computational time for texture detection and recognition. CA has also been applied in social sciences [23].…”

Section: Background On Correspondence Analysismentioning

confidence: 99%

Textual features for corpus visualization using correspondence analysis

Bašić

Morin

Zupan

et al. 2009

IDA

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Explorative data analysis in text mining essentially relies on effective visualization techniques which can expose hidden relationships among documents and reveal correspondence between documents and their features. In text mining, the documents are most often represented by feature vectors of very high dimensions, requiring dimensionality reduction to obtain visual projections in two-or three-dimensional space. Correspondence analysis is an unsupervised approach that allows for construction of low-dimensional projection space with simultaneous placement of both documents and features, making it ideal for explorative analysis in text mining. Its present use, however, has been limited to word-based features. In this paper, we investigate how this particular document representation compares to the representation with letter n-grams and word n-grams, and find that these alternative representations yield better results in separating documents of different class. We perform our experimental analysis on a bilingual Croatian-English parallel corpus, allowing us to additionally explore the impact of features in different languages on the quality of visualizations.

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Section: Background On Correspondence Analysismentioning

confidence: 99%

Textual features for corpus visualization using correspondence analysis

Bašić

Morin

Zupan

et al. 2009

IDA

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“…It can reveal the difference between the various categories of the same variable, as well as the correspondence relations between the various categories of the different variables by analyzing qualitative or quantitative variables which constitute the interaction matrix to reveal the relations among variables [2][3][4]. In this paper, the correspondence analysis is employed to analyze the relationship between different features in order to obtain the image features.…”

Section: Introductionmentioning

confidence: 99%

Integrating multi-feature of image based on correspondence analysis

Dai

Haimei

Han

2010

2010 5th IEEE Conference on Industrial Electronics and Applications

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Image feature detection is one of the key techniques of image analysis and image understanding. For a given image, due to the differences in feature extraction methods, the results of feature extraction are not the same. Some methods may results in the loss of certain features of the image, while some may generate extra features of the image. How to integrate the features obtained by different feature extraction methods for a given image to gain a satisfactory result of feature extraction is a very important research topic. In this paper, we employ correspondence analysis theory to integrate image features obtained by using several feature extraction methods for the image. Firstly, the image features are extracted by using different feature extraction methods, and they are arranged as columns to form a data matrix to be analyzed. Secondly, the score of each pixel in the image is calculated by using correspondence analysis for the data matrix. Finally, significance analysis is made of score vectors and the integrated image features are obtained according to the degree of significance. Experiment results illustrate the efficiency of the presented method in this paper.

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Object detection and segmentation in camouflaged environments

Makrogiannis

San-Martin

2005

SPIE Proceedings

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Correspondence analysis applied to textural features recognition

Cited by 3 publications

References 2 publications

Textual features for corpus visualization using correspondence analysis

Textual features for corpus visualization using correspondence analysis

Integrating multi-feature of image based on correspondence analysis

Object detection and segmentation in camouflaged environments

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