A Study on Discrete Wavelet Transform based

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components of level 2 decomposed images to extract the texture ... that are natural in the images, color, shape and texture. ... Available online@www.ijcta.com.
ISSN:2229-6093

P Manikandaprabhu et al, Int.J.Computer Technology & Applications,Vol 5 (5),1805-1811

A Study on Discrete Wavelet Transform based Texture Feature Extraction for Image Mining Dr. T. Karthikeyan1, P. Manikandaprabhu2 1 Associate Professor, 2 Research Scholar, Department of Computer Science, PSG College of Arts & Science, Coimbatore. 2 [email protected]

Abstract This paper proposed the discrete wavelet based texture features in Image Mining. The Proposed methodology uses the Discrete Wavelet Transform to reduce the size of test images. Grey Level Co-occurrence Matrix (GLCM) is applied for all test images of Low Level components of level 2 decomposed images to extract the texture feature of the images. Related images are retrieved by using different distance measure classifiers. The experimental result shows that the proposed method achieves comparable retrieval performance for correlation property of GLCM of texture feature.

Fig. 1 shows the architecture of a typical CBIR system. For each image in the image database and its image features are extracted and the obtained feature space (or vector) is stored in the feature database. once a query image comes in, its feature space are going to be compared with those within the feature database one by one and the similar images with the smallest feature distance will be retrieved. Image DB

Query Image

Feature Extraction

Keywords - Texture, Discrete Wavelet Transform, gray level co-occurrence matrix, Distance Measures.

1. Introduction The open spread use of digital and multimedia knowledge, storeroom; finding and recovery of images beginning the huge database become not easy. To facilitate economical searching and retrieving of pictures as of the digital collection, new software and techniques have been emerged. The need to discover a preferred image from a huge collection is mutual by many skilled groups including the media persons, drawing engineers, art historians and scholars etc. Content Based Image Retrieval (CBIR) is compared with text or content related advance for recover similar images from the database [24, 25]. Content Based Image Retrieval (CBIR) does not need manual annotation for each image and is not incomplete by the availability of lexicons as a substitute this framework utilizes the low level features that are natural in the images, color, shape and texture. In CBIR, some forms of parallel between images are computed using image futures extracted from them. Thus, users can look for images just like query images quickly and effectively.

Feature Database

Query Features

Similarity Measures

Retrieved Images

Fig.1: Image Retrieval Process CBIR may be divided in the following stages: • Preprocessing: The image is first processed in order to extract the features to describe the contents. The processing involves normalization, filtering, segmentation and object identification. The output of this stage could be a set of significant regions and objects. • Feature extraction: Features such as color, shape, texture, etc. are used to describe the content of the image. Image features can be classified into primitives.

2. Feature Extraction For the given image database [1], features are extracted first from individual images. The visual features like color, shape, texture or spatial features or

IJCTA | Sept-Oct 2014 Available [email protected]

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P Manikandaprabhu et al, Int.J.Computer Technology & Applications,Vol 5 (5),1805-1811

some compressed domain features. The extracted features are delineating the feature vectors. These feature vectors are then stored to form image feature database. For a given query image, we similarly extract its features and form a feature vector. This feature vector is matched with the already stored vectors in image feature database. Sometimes dimensionality reduction techniques are employed to reduce the computations. The distance between the feature vector of the query image and those of the images in the database is then calculated. The distance of a query image with itself is zero if it is in database. Then, the distances are stored in increasing order and retrieval is performed with the help of indexing scheme. The feature is distinct as a role of one or more capacity, every of which specifies some experimental property of an object and it quantifies some significant characteristics of the object. We classify the various features currently employed as follows: • General features: Function self-regulating features such as shape, color and texture. Independent of the abstraction level, they can be advance in divided into: - Pixel level: Features considered at each pixel level, e.g. location, colour. - Local features: Features considered above the outcome of results is subdivision of the image band on image segmentation or edge detection. - Global level features: Features measured over the whole image or simply expected sub-area of an image. • Domain-specific level: Application reliant features like human faces, fingerprints, and conceptual features. These features are typically a synthesis of low-level features for a some specific domain. On the other hand, all features can be closely secret into low level features and high level features. Low level features can be extracted directly from the original images, whereas high-level feature extraction must be based on low level features [2]. The vital problems of content based image retrieval system, which are: i. Image database selection, ii. Similarity measurement, iii. Performance evaluation of the retrieval process and iv. Low-level image features extraction.

3. Wavelet Transform Wavelet transform has a good location property in time and frequency domain and is exactly within the direction of transform compression idea. The discrete wavelet transforms states to wavelet transforms that the wavelets are disjointedly appraised. A transform which

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limits a function both in space and scaling and has some necessary properties compared to the Fourier transform. The transform is centred on a wavelet matrix, which can be figured more quickly than the analogous Fourier matrix. Most notably, the DWT is used for signal coding, where the assets of the transform are exploited to signify a discrete signal in an extra redundant form, often as a preconditioning for data compression. The discrete wavelet transform has a vast quantity of applications in Science, Computer Science, Mathematics and Engineering. Wavelets are functions that satisfy certain mathematical requirements and are used in representing data or other functions. The basic awareness of the wavelet transform is to exemplify any arbitrary signal “X” as a superposition of a regular of such wavelets or basis functions. These basis functions are gained from a single photo type wavelet called the mother wavelet by dilation (scaling) and translation (shifts). The discrete wavelet transform for two dimensional signals can be defined as follows.

w(a1 , a2 , b1 , b 2 ) 

 X  b1 Y  b2  1  ,  a2  a  a1

(1)

Where, a= a1a2 The indexes equation.(1) w (a1, a2, b1, b2) are called wavelet coefficients of signal X and a1, a2 are dilation & b1, b2 are translation, ψ is the transforming function is known as mother wavelet. Low frequencies are examined with low temporal resolution while high frequencies with more temporal resolution. A wavelet transform combines both low pass and high pass filtering in spectral decomposition of signals. In case of discrete wavelet, the image is decomposed into a discrete set of wavelet coefficients using an orthogonal set of basic functions. These sets are divided into four parts such as approximation, horizontal details, vertical details and diagonal details. Discrete Wavelet transform [3] provide substantial improvement in picture quality at higher compression ratio. The Embedded Zero tree Wavelet coding is a simple, effective progressive image coding algorithm and can be worn for both lossless and lossy compression systems. This algorithm works well with the proposed coding scheme because the zero tree structure is effective in describing the significance map of the transform coefficients, as it exploits the inherent selfsimilarity of the subband image over the range of scales, and the positioning of majority of zero valued coefficients in the higher frequency subbands. The EZW algorithm applies Successive Approximation Quantization in order to provide multi-precision representation of the transformed coefficients and to facilitate the embedded coding. The algorithm codes

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the transformed coefficients in decreasing order in several scans. Each scan of the algorithm consists of two passes: significant map encoding and refinement pass. The dominant pass scans the subband structure in zigzag, right-to-left and then top-to-bottom within each scale, before proceeding to the next higher scale of subband structure as presented in Fig .2. For each and every pass, a threshold (T) is chosen against which all the coefficients are measured and encoded as one of the following four symbols,  Significant positive – If the coefficient value is greater than threshold T  Significant negative – If the magnitude of the coefficient value is greater than threshold T  Zero tree root – A coefficient is encoded as zero tree root if the coefficient and all its descendants are insignificant with respect to threshold T  Isolated zero – If the coefficient is insignificant but some of its descendants are significant.

T0  2

log2 Cmax

(2) where equation.(2) Cmax is the maximum coefficient in the subband structure. The successive approximation quantization uses a monotonically decreasing set of thresholds and encodes the transformed coefficients as one of the above four labels with respect to any given threshold. For successive significant pass encoding, the T threshold is lowered as T K  K  1 and only those 2 coefficients not yet found to be significant in the previous pass are scanned for encoding, and the process is repeated until the threshold reaches zero, and results in complete encoded bit streams.

tree, is then compared to a particular threshold T. If the magnitude of all the wavelet coefficients/pixels in the tree are smaller than T, the entire tree structure (that is the root and all its descendant nodes) is coded by one symbol, the zerotree symbol ZTR. If however, there exit significant wavelet coefficients/pixels, then the tree root is coded as being significant or insignificant, if its magnitude is larger than or smaller than T, respectively. The descendant nodes are then each examined in turn to determine whether each is the root of a possible sub zero tree structure, or not. This process is carried out such that all the nodes in all the trees are examined for possible sub zero tree structures. The significant wavelet coefficients/pixels in a tree are coded by one of two symbols, POS or NEG, depending on whether their actual values are positive or negative, respectively. The process of classifying the pixels as being ZTR, IZ, POS, or NEG is referred to as the dominant pass in [4]. This is then followed by the subordinate pass in which the significant wavelet coefficients/pixels in the image are refined by determining whether their magnitudes lie within the intervals (T, 3T/2) and (3T/2,2T). Those wavelet coefficients/pixels whose magnitudes lie in the interval (T, 3T/2) are represented by a 0 (LOW), whereas those with magnitudes lying in the interval (3T/2,2T) are represented by a 1 (HIGH). Subsequent to the completion of both the dominant and subordinate passes, the threshold value T is reduced by a factor of 2, and the entire process repeated. This coding strategy, consisting of the dominant and subordinate passes followed by the reduction in the threshold value, is iterated until a target bit rate is achieved. The root node of each tree is located at the highest level of the decomposition pyramid, and all its descendants are located in different spatial frequency bands at the same pyramid level, or clustered in groups of 2 X 2 at lower levels of the decomposition pyramid. An EZW decoder reconstructs the image by progressively updating the values of each wavelet coefficient/pixel in a tree as it receives the data. The decoder's decisions are always synchronized to those of the encoder.

4. Texture Features Fig.2: EZW subband structure scanning order In the embedded zero tree wavelet coding strategy, developed by Shapiro, a wavelet/subband decomposition of the image is performed. The wavelet coefficients/pixels are then grouped into Spatial Orientation Trees. The magnitude of each wavelet coefficients/pixels in a tree, starting with the root of the

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Among totally different visual characteristics like color and shape for the analysis of various types of images, texture is reported to be outstanding and very important low level feature [5, 6]. Even though no standard definition exists for texture, Sklansky [7] outlined the texture collection of native properties among the image region with a continuing, slowly varied or about periodic pattern. Texture gives the information on structural arrangement of surfaces and

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objects on the image. Texture is not defined for a separate pixel; it depends on the distribution of intensity over the image. Texture possesses regularity and scalability properties; it is represented by main directions, contrast and sharpness. It is measured using its distinct properties like periodicity, coarseness, directionality and pattern complexity for efficient image retrieval particularly on the aspects of orientation and scale [8]. Tuceryan and Jain [9] divided the different methods for feature extraction into four main categories, namely: structural, statistical, model-based and transform domain. Basically, texture representation methods can be classified into two categories: structural and statistical. Statistical methods, including Fourier power spectra, co-occurrence matrices, shift-invariant principal component analysis (SPCA), Tamura features, Wold decomposition[10], Markov random field[11], fractal model[12] and multi-resolution filtering techniques such as Gabor[13] and wavelet transform[14], characterize texture by the statistical distribution of the image intensity. D. A. Clausi et. al [15] designed the fusion texture feature with Gabor filter and co occurrence probabilities for texture segmentation and demonstrated that it outperforms well for noisy images and the high dimensional feature vector. The DWT based color cooccurrence feature for texture classification is explained in [16]. Haralick et. al [17] proposed the methods for representing texture features of images was grey level co-occurrence matrices (GLCM). Haralick et. al [17] also suggested 14 descriptors including the contrast, correlation, entropy and others. Each descriptor shows one texture property. Therefore, many works for example as described in [18], are devoted to selecting those statistical descriptors derived from the cooccurrence matrices that describe texture within the best approach. In [19], firstly, transforming color space from RGB model to HSI model and then extracting color histogram to form color feature vector. Secondly, extracting the texture feature by using gray cooccurrence matrix. The texture of image is an illustration of spatial relationship of gray level image. Co-occurrence matrix is make it up based on the point of reference and distance between image pixels. The co-occurrence matrix C(i, j) counts the co-occurrence of pixels with gray values i and j at a given distance d. The distance d is outlined in polar coordinates (d,  ), with discrete length and orientation. In practice,  takes the values 0◦; 45◦; 90◦; 135◦; 180◦; 225◦; 270◦; and 315◦. The cooccurrence matrix C(i, j) can now be defined as follows:

IJCTA | Sept-Oct 2014 Available [email protected]

(( x1 , y1 ),( x2 , y2 ))  ( XY )  ( XY )   for f ( x1 , y1 )  i, f ( x2 , y2 )  j   C (i, j )  card   (3) ( x , y )  ( x , y )  ( d cos  , d sin  ); 1 1  2 2   for 0