Image Data Mining Based on Wavelet Transform for Visualization of the Unique Characteristics of Image Data

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)
DOI: 10.4018/978-1-4666-8654-0.ch001
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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.
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1. Introduction

Data Mining (DM) is a process of automatically extracting novel, useful, and understandable patterns from a large collection of data. Over the past decade, this area has become significant in many fields naming from the retailer -marketing to DNA -bioinformatics. DM techniques involve diverse dynamic and advanced tools, including wavelet to explore data sets as its nature and domain contexts (Jiawei, 2012). Wavelet theory could naturally play an important role in DM because it could provide data presentations that enable efficient and accurate mining process, which incorporated into the kernel for many algorithms. Standard wavelet applications are mainly powerful for temporal-spatial data, which involves time-series, stream, and image data (Yu, Huang & Xia, 2012; Pimwadee, Aryya, George & Zhiyuan, 2011). It has been successfully applied to analyze large-scale image data using DM techniques. The approach is introducing a novel methodology how to reduce the amount of manual labor that usually comes with visualize and characterize big image data collections. With numeric and textual data, the techniques of extracting useful information from unstructured data have already been more or less established. However, with image-heavy datasets, processing methods such as object detection and text recognition are complex to be reliable and in most cases do not stand up to a comparison with a human doing the work.

Image mining is the process of searching and discovering valuable information and knowledge in large volumes of image data. It draws basic principles from Databases, Machine Learning (ML), Statistics, Pattern Recognition (PR) and 'soft' computing, which help to use Wavelet Transform (WT) based DM techniques enables a more efficient methods of the image data discrimination and analysis (Tao, Qi, Shenghuo & Mitsunori, 2002). However, image processing is one of those things people are still much better at than computers that use to know something new. Therefore, based on these two facts, we proposed WT based DM for visualization and characterization of the unique feature of image data. Besides DM algorithms, wavelet technique is growing importantly and having a lot of advantages that already exist numerous successful applications in image mining. The WT is syntheses of idea’s computational methodologies are based orthonormal wavelet basis, which is fundamental to decompose, segments, extract and handling image data. WT based DM techniques, functions, or operators into different frequency components, the methodologies and image features' component with a resolution matched to its scale discussed in details.

The chapter is organized as first the introduction followed by section 2 about the related works, which focused on the facts and advancement of image DM in different approaches. It also includes image data managements and its attributes, segmentations, mining algorithms, distributed computing and others. In section 3, the wavelet technology, methodology and approaches are discussed. Section 4 discussed wavelet-based image annotations and measurements to visualize and characterize the detail and unique features of image data and mining techniques. In section 5, we present the summary of the chapter, which followed by the acknowledgment of the supporters of the chapter works and list of cited references.

Key Terms in this Chapter

Image Data: Is a photographic or trace objects that represent the underlying pixel data of an area of an image element, which is created, collected and stored using image constructor devices.

Image Retrieval: Is a process of searching for digital images in large image scale image data, which is computer based for browsing, searching and retrieving images from digital images.

Image Characterization: Is the method present for estimating the complexity of an image based on objects or texts real contexts, which provides a means for classifying and evaluating the object features by way of their visual representations.

Image Segmentation: Is the process of clustering or partitioning a digital image features into multiple sets of pixels to simplify or change the representation of an image into something, which understandable to more meaningful and easier to identify objects or other relevant information in digital formats.

Visualization: Is any technique for creating images, diagrams, or animations to communicate a message in which both abstract and concrete ideas. It means that the data must come from something that is abstract or at least not immediately visible (from the inside of the human body). This rule out photography and image processing.

Feature Extractions: 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.

Wavelet Transforms: Are a mathematical means for performing signal or wave-like oscillation with an amplitude analysis when the signal frequency varies over time. It is purposefully crafted to have specific properties that make them useful for signal processing, which provode a “reverse, shift, multiply and integrate” technique called convolution, with portions of a known signal to extract information from the unknown signal.

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