Md. Khayrul Bashar

Exploring duplicated regions in natural images.

Duplication of image regions is a common method for manipulating original images, using typical software like Adobe Photoshop, 3DS MAX, etc. In this study, we propose a duplication detection approach that can adopt two robust features based on discrete wavelet transform (DWT) and kernel principal component analysis (KPCA). Both schemes provide excellent representations of the image data for robust block matching. Multiresolution wavelet coefficients and KPCA-based projected vectors corresponding to image-blocks are arranged into a matrix for lexicographic sorting. Sorted blocks are used for making a list of similar point-pairs and for computing their offset frequencies. Duplicated regions are then segmented by an automatic technique that refines the list of corresponding point-pairs and eliminates the minimum offset-frequency threshold parameter in the usual detection method. A new technique that extends the basic algorithm for detecting Flip and Rotation types of forgeries is also proposed. This method uses global geometric transformation and the labeling technique to indentify the mentioned forgeries. Experiments with a good number of natural images show very promising results, when compared with the conventional PCA-based approach. A quantitative analysis indicate that the wavelet-based feature outperforms PCA- or KPCA-based features in terms of average precision and recall in the noiseless, or uncompressed domain, while KPCA-based feature obtains excellent performance in the additive noise and lossy JPEG compression environments.

Automatic detection of informative frames from wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is a new clinical technology permitting visualization of the small bowel, the most difficult segment of the digestive tract. The major drawback of this technology is the excessive amount of time required for video diagnosis. We therefore propose a method for generating smaller videos by detecting informative frames from original WCE videos. This method isolates useless frames that are highly contaminated by turbid fluids, faecal materials and/or residual foods. These materials and fluids are presented in a wide range of colors, from brown to yellow, and/or have bubble-like texture patterns. The detection scheme therefore consists of two steps: isolating (Step-1) highly contaminated non-bubbled (HCN) frames and (Step-2) significantly bubbled (SB) frames. Two color representations, viz., local color moments in Ohta space and the HSV color histogram, are attempted to characterize HCN frames, which are isolated by a support vector machine (SVM) classifier in Step-1. The rest of the frames go to Step-2, where a Gauss Laguerre transform (GLT) based multiresolution texture feature is used to characterize the bubble structures in WCE frames. GLT uses Laguerre Gauss circular harmonic functions (LG-CHFs) to decompose WCE images into multiresolution components. An automatic method of segmentation was designed to extract bubbled regions from grayscale versions of the color images based on the local absolute energies of their CHF responses. The final informative frames were detected by using a threshold on the segmented regions. An automatic procedure for selecting features based on analyzing the consistency of the energy-contrast map is also proposed. Three experiments, two of which use 14,841 and 37,100 frames from three videos and the rest uses 66,582 frames from six videos, were conducted for justifying the proposed method. The two combinations of the proposed color and texture features showed excellent average detection accuracies (86.42% and 84.45%) with the final experiment, when compared with the same color features followed by conventional Gabor-based (78.18% and 76.29%) and discrete wavelet-based (65.43% and 63.83%) texture features. Although intra-video training-testing cases are typical choices for supervised classification in Step-1, combining a suitable number of training sets using a subset of the input videos was shown to be possible. This mixing not only reduced computation costs but also produced better detection accuracies by minimizing visual-selection errors, especially when processing large numbers of WCE videos.

Detecting Informative Frames from Wireless Capsule Endoscopic Video Using Color and Texture Features

Despite emerging technology, wireless capsule endoscopy needs high amount of diagnosis-time due to the presence of many useless frames, created by turbid fluids, foods, and faecal materials. These materials and fluids present a wide range of colors and/or bubble-like texture patterns. We, therefore, propose a cascade method for informative frame detection, which uses local color histogram to isolate highly contaminated non-bubbled (HCN) frames, and Gauss Laguerre Transform (GLT) based multiresolution norm-1 energy feature to isolate significantly bubbled (SB) frames. Supervised support vector machine is used to classify HCN frames (Stage-1), while automatic bubble segmentation followed by threshold operation (Stage-2) is adopted to detect informative frames by isolating SB frames. An experiment with 20,558 frames from the three videos shows 97.48% average detection accuracy by the proposed method, when compared with methods adopting Gabor based-(75.52%) and discrete wavelet based features (63.15%) with the same color feature.

Wavelet transform-based locally orderless images for texture segmentation

Texture analysis is an important issue in many areas like object recognition, image retrieval study, medical imaging, robotics, and remote sensing. Despite the development of a family of techniques over the last couple of decades, there are only a few reliable methods. Multiresolution techniques seem to be attractive for many applications. In this study, we present an approach based on the discrete wavelet transform and scale space concept. We integrate the framework of locally orderless images (LOIs) with the transform coefficients to obtain a flexible method for texture segmentation. Compared to intensity (spatial domain), the wavelet coefficients appear to be more reliable with respect to noise immunity and the ease of feature formation. Hence, we represent each discrete coefficient value with a probability density function to form isophote images. Each isophote image is then convolved with a Gaussian to form LOIs, which specify a local histogram in each transform point. These LOIs, or statistical moments computed from LOIs, can be regarded as texture features. An experiment with the standard Brodatzs and VisTex texture databases demonstrates the superior performance of the wavelet-based LOIs compared to conventional LOI-based moments or wavelet and Gabor energy features. The elegance of the approach is in the relatively greater flexibility in producing segmentation results. A simple minimum distance classifier and confusion matrix analyses confirm the above attributes.

Image retrieval by pattern categorization using wavelet domain perceptual features with LVQ neural network

For the efficient and cost effective management of large volume of images in textile industry, an effective retrieval system is expected. Textile (e.g., curtain) images of raw clothes have wide varieties of design patterns. Despite many research works in this area, only a few emphasize on complex pattern characteristics. Such patterns are horizontal, vertical, cross-stripes, leaves and flowers in curtain database. In this study, we propose a system that retrieves images based on wavelet domain perceptual features which mainly depend on edge and correlation characteristics of the wavelet sub-bands in the major directions (horizontal and vertical). In order to reduce searching time, we first catagorize various patterns using supervised learning vector quantization (LVQ) technique. Then for each category or group, a prototype vector is formed by averaging all classified feature vectors in it. For a typical query, the query key is first compared with a few prototype vectors to determine the expected category. Then the query key performs similarity comparisons with the population of that particular group and retrieves relevant images. Users have also the provision to select subsequent similar groups if any query fails to capture the correct group at first attempt. An experiment with a set of curtain images shows the effectiveness of the proposed features compared to conventional Gabor, pyramidal wavelet transform (PWT) or local binary pattern (LBP) features. wavelet transform (PWT) or local binary pattern (LBP) features.

A new texture representation approach based on local feature saliency

Although it has been studied in some depth, texture characterization is still a challenging issue for real-life applications. In this study, we propose a multiresolution salient-point-based approach in the wavelet domain. This incorporates a two-phase feature extraction scheme. In the first phase, each wavelet subband (LH, HL, or HH) is used to compute local features by using multidisciplined (statistical, geometrical, or fractal) existing texture measures. These features are converted into binary images, called salient point images (SPIs), via threshold operation. This operation is the key step in our approach because it provides an opportunity for better segmentation and combination of multiple features. In the final phase, we propose a set of new texture features, namely, salient-point density (SPD), non-salient-point density (NSPD), salient-point residual (SPR), saliency and non-saliency product (SNP), and salient-point distribution non-uniformity (SPDN). These features characterize various aspects of image texture such as fineness/coarseness, primitive distribution, internal structures, etc. These features are then applied to the well-known K-means algorithm for unsupervised segmentation of texture images. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potential of the proposed features compared to the wavelet energy (WE) and local extrema density feature (LED).

WAVELET-BASED SALIENT ENERGY POINTS FOR UNSUPERVISED TEXTURE SEGMENTATION

Despite extensive research on image texture analysis, it is still problematic to characterize and segment texture images especially in the presence of complex patterns. Upon tremendous advancement of the internet and the digital technology, there is also a need for the development of simple but efficient algorithms, which can be adaptable to real-time systems. In this study, we propose such an approach based on multiresolution discrete wavelet transform (DWT). After the transform, we compute salient energy points from each directional sub-band (LH, HL, and HH) in the form of binary image by thresholding intermittency indices of wavelet coefficients. We then propose and extract two new texture features namely Salient Point Density (SPD) and Salient Point Distribution Nonuniformity (SPDN) based on the number and the distribution of salient pixels in the local neighborhood of every pixel of the multiscale binary images. We thus obtain a set of feature images, which are subsequently applied to the popular K-means algorithm for the unsupervised segmentation of texture images. Though the above representation appear simple and infrequent in the literature, it proves useful in the context of texture segmentation. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potentiality of the proposed approach.

Fusing cortex transform and intensity based features for image texture classification

This paper proposes a new scheme of fusing cortex transform and brightness based features obtained by local windowing operation. Energy features are obtained by applying popular cortex transform technique within a sliding window rather than the conventional way, while we define three features namely directional surface density (DSD), normalised sharpness index (NSI), and normalized frequency index (NFI) as measures for pixel brightness variation. Fusion by simply vector tagging as well as by correlation is performed in the feature space and then classification is done using minimum distance classifier on the fused vectors. It is interesting that the brightness features, though inferior on some natural images, often produces smoother texture boundary in mosaic images, whereas energy features show the opposite behavior. This symmetrically inverse property is combined through vector fusion for robust classification of multi-texture images obtained from Brodatz album and VisTex database. Classification outcome with confusion matrix analysis shows the robustness of the scheme.

Image retrieval by local contrast patterns and color

Despite simplicity of the Local binary patterns (LBP) or local edge patterns (LEP) for texture description, they do not always convey complex pattern information. Moreover they are susceptive to various image distortions. Hence we propose a new descriptor called Local Contrast Patterns(LCP), which encode the joint difference distribution of filter responses that can be effectively computed by the higher order directional Gaussian derivatives. Though statistical moments of the filter responses are typical texture features, various complex patterns ( e.g., edges, points, blobs) are well captured by the proposed LCP. Observation shows that anyone of the first few derivatives can produce promising results compared to LBP(or LEP). To extract more improved outcome, two sub-optimal descriptors (LCP1, LCP2) are computed by maximizing local bit frequency and local contrast-ratio. Global RGB color histogram is then combined with the proposed LCP descriptors for color-texture retrieval. Experiments with the grayscale (Brodatz album) and color-texture (MIT VisTex) databases show that our proposed LCP (LCP+RGB) produces 8 % and 2.1 % (1.4 % and 1.9 % ) improved recall rates compared to LBP and LEP (LBP+RGB and LEP+RGB) features. The achievement of the lowest rank ratio, i.e., 2.789 for gray images (1.482 for color images) also indicates the potentiality of the proposed LCP2(LCP2+RGB) feature.

Informative frame detection from wireless capsule video endoscopic images

Wireless capsule endoscopy (WCE) is a new clinical technology permitting the visualization of the small bowel, the most difficult segment of the digestive tract. The major drawback of this technology is the high amount of time for video diagnosis. In this study, we propose a method for informative frame detection by isolating useless frames that are substantially covered by turbid fluids or their contamination with other materials, e.g., faecal, semi-processed or unabsorbed foods etc. Such materials and fluids present a wide range of colors, from brown to yellow, and/or bubble-like texture patterns. The detection scheme, therefore, consists of two stages: highly contaminated non-bubbled (HCN) frame detection and significantly bubbled (SB) frame detection. Local color moments in the Ohta color space are used to characterize HCN frames, which are isolated by the Support Vector Machine (SVM) classifier in Stage-1. The rest of the frames go to the Stage-2, where Laguerre gauss Circular Harmonic Functions (LG-CHFs) extract the characteristics of the bubble-structures in a multi-resolution framework. An automatic segmentation method is designed to extract the bubbled regions based on local absolute energies of the CHF responses, derived from the grayscale version of the original color image. Final detection of the informative frames is obtained by using threshold operation on the extracted regions. An experiment with 20,558 frames from the three videos shows the excellent average detection accuracy (96.75%) by the proposed method, when compared with the Gabor based- (74.29%) and discrete wavelet based features (62.21%).