Abstract
This paper presents texture feature extraction and selection methods for on-line pattern classification evaluation. Feature selection for texture analysis plays a vital role in the field of image recognition. Despite many approaches done previously, this research is entirely different from them since it comes from the fundamental ideas of feature selection for image retrieval. The proposed approach is capable of selecting the best features without recourse to classification and segmentation procedures. In this approach, probability density function estimation and a modified Bhattacharyya distance method are applied for clustering texture features of images in sequences and for comparing multi-distributed clusters with one another, respectively.
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Win, K., Baik, S., Baik, R., Ahn, S., Kim, S., Jo, Y. (2004). Texture Feature Extraction and Selection for Classification of Images in a Sequence. In: Klette, R., Žunić, J. (eds) Combinatorial Image Analysis. IWCIA 2004. Lecture Notes in Computer Science, vol 3322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30503-3_58
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DOI: https://doi.org/10.1007/978-3-540-30503-3_58
Publisher Name: Springer, Berlin, Heidelberg
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