TOWARDS CLASSIFYING CLASSICAL BATIK IMAGES - Unpar

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training images off-line online new image image preprocessing image preprocessing texture and shape features generation texture and shape features generation Figure 2. A schematic of classical batik images classification system. Images Preprocessing. The pre-processing steps are: cropping the images to find the part of the images that are meaningful (in the experiment, the cropped size is 128 x 128 pixels), converting them to gray-scale images. Texture Features Generation. Based on experiments, (Randen, Husoy 1999) concludes that there is no superior method for texture classification. Some methods may be good for certain images, but inappropriate for other images, with respect to the classification error. In this study, two methods would be implemented, which are discrete wavelet transform (DWT) and discrete cosine transformed (DCT) based. For each method, two approaches would be implemented: content-based and region-content-based. Content-based steps: (1) 2-level DWT (or DCT) is applied to an image. (2) Statistical measures (mean and variance) are computed from the coefficient resulted from Step 1 and stored as the image feature. Region-content-based steps: (1) Each image is segmented into windows (which could be overlapping). (2) 2-level DWT (or DCT) is applied to each window and the coefficients are normalized. (3) The result of Step 2 are clustered using k-Means algorithm. (4) Mean and variance of each cluster are computed and stored as the feature. These methods are the modification version of the techniques employed in (Wang, et.all, 1997), (Bartolini., 2001), (Feng, Jiang, 2002) and (Singh, 2003). As most images are stored as compressed form of JPEG format, the advantage of using DCT is that the features can be directly generated from the compressed form (without decompressing the images first), which reduces the computation time. Shape Feature Generation. As shown on Figure 1, batik motifs, which are used to determine the batik class, are “hidden” among isen-isen. Therefore, making sure that motif shapes could be obtained is important. Here, the intensity of images is adjusted so that the motif shapes are clear. Next, to generate shape features, three methods are proposed: enhanced Fourier descriptor (Zhang, Lu, 2002), moment invariants (Lu, 1999) and elastic template matching (Hirata, Kato, 4 image features and classes classification image class
1992). Fourier descriptor methods: (1) The image is presented to Canny edge detector. (2) The centroid of the edge points is computed. (3) Compute the distance of each sample edge point to the centroid. (4) Apply Discrete Fourier Transform to the result of step 3. (5) Normalize the coefficients of step 4 by dividing each coefficient (Fi) with the first coefficient (F0) and store them as the features. Moments invariant method: Three order moments are computed from the preprocessed images and the result are stored as the feature. Elastic template matching methods: (1) The image is presented to Canny edge detector. (2) The output of step 1 (an array containing 1s and 0s) is stored as the feature. Classification using k-Nearest Neighbor. The basic idea of the classification process is adopted from (Sheikholestami et.all, 1998) with some modification. The classification process consists of four main steps: (1) Compute the new image shape and texture features and the similarity values between the new image and all training images using shape and texture features. Find two sets of training images that are most similar to the new image in terms of the ornaments shape and the image texture. (2) Combine the two sets resulting in step 1. Let Rq be the new set. (3) Compute the overall image similarities of images in Rq using multi-layer back-propagation neural network. (4) Sort the new similarity values obtained from step (3), and using k-nearest neighbor classification technique, determine the class of the new image. Computing image similarities. Texture feature. For content-based, the distance between 2 images is computed using Euclidean distance. Then the distance is transformed into similarity value as follows: SimValue(i,j) = 1 – distance(i,j) / max of Distance (1) where i,j denoting the index of the two images being compared and Distance is a set of all distance between images. For region-content-based, the method in (Bartolini, 2001) is applied. Shape feature. The main steps are: (1) The new image is segmented into windows and features are generated from each window. (2) The feature of a training image is compared to the feature of each new image windows. (3) For moments invariant and Fourier descriptor, the similarity value is computed using Eq. 1. For elastic template matching: similarity is computed by determining the correlation between the two features. The largest value among all similarity values of the windows is selected as the similarity value between the new image and training image. 5
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