Is a process of transforming a distinctive characteristic of an arbitrary data, such as text or images into numerical features that usable for machine learning. The process starts with an initial set of measured data and builds derived values (features) intended to be informative, non-redundant, which facilitating the subsequent learning and generalization steps, in some cases leading to better human interpretations that related to dimensionality reduction. It is the technique of dealing the image characterizations and segmentations of the big image into smaller windows that the features are easily extracted.
Published in Chapter:
Image Data Mining Based on Wavelet Transform for Visualization of the Unique Characteristics of Image Data
Gebeyehu Belay Gebremeskel (Chongqing University, China), Yi Chai (Chongqing University, China), Zhou Shangbo (Chongqing University, China), and Su Xu (Chongqing University, China)
Copyright: © 2016
|Pages: 40
DOI: 10.4018/978-1-4666-8654-0.ch001
Abstract
Mining techniques can play an important role in image decomposition, segmentation, classification and retrieval systems. As image data become more complex and growing at a fast pace, searching valuable information and knowledge implicit become more challenging than ever before. In this chapter, authors proposed a WT based DM techniques to optimize and characterize the unique feature of image retrieval, which is fundamental to optimize informative mathematical representation of image objects. Many software, including data exploratory tools such as DM packages contain fast and efficient programs that perform WT. Wavelets have quickly gained popularity among scientists and engineers, both in theoretical research and in applications. The authors discussed in details and introduced a novel method for image database analysis in different scenarios that foster the wide access of image data.