Zahid Mehmood

Image retrieval by addition of spatial information based on histograms of triangular regions

The addition of spatial information to the inverted index of the BoF representation.Image representation in the form of triangular histograms.Three different classifiers are evaluated in order to determine the best performance of the proposed work. Display Omitted The compositional and content attributes of images carry information that enhances the performance of image retrieval. Standard images are constructed by following the rule of thirds that divides an image into nine equal parts by placing objects or regions of interest at the intersecting lines of the grid. An image represents regions and objects that are in a spatial semantic relationship with respect to each other. While the Bag of Features (BoF) representation is commonly used for image retrieval, it lacks spatial information. In this paper, we present two novel image representation methods based on the histograms of triangles, which add spatial information to the inverted index of BoF representation. Histograms of triangles are computed at two levels, by dividing an image into two and four triangles that are evaluated separately. Extensive experiments and comparisons conducted on two datasets demonstrate that the proposed image representations enhance the performance of image retrieval.

A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words

Content-based image retrieval (CBIR) provides a sustainable solution to retrieve similar images from an image archive. In the last few years, the Bag-of-Visual-Words (BoVW) model gained attention and significantly improved the performance of image retrieval. In the standard BoVW model, an image is represented as an orderless global histogram of visual words by ignoring the spatial layout. The spatial layout of an image carries significant information that can enhance the performance of CBIR. In this paper, we are presenting a novel image representation that is based on a combination of local and global histograms of visual words. The global histogram of visual words is constructed over the whole image, while the local histogram of visual words is constructed over the local rectangular region of the image. The local histogram contains the spatial information about the salient objects. Extensive experiments and comparisons conducted on Corel-A, Caltech-256, and Ground Truth image datasets demonstrate that the proposed image representation increases the performance of image retrieval.

Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine

In recent years, the rapid growth of multimedia content makes content-based image retrieval (CBIR) a challenging research problem. The content-based attributes of the image are associated with the position of objects and regions within the image. The addition of image content-based attributes to image retrieval enhances its performance. In the last few years, the bag-of-visual-words (BoVW) based image representation model gained attention and significantly improved the efficiency and effectiveness of CBIR. In BoVW-based image representation model, an image is represented as an order-less histogram of visual words by ignoring the spatial attributes. In this paper, we present a novel image representation based on the weighted average of triangular histograms (WATH) of visual words. The proposed approach adds the image spatial contents to the inverted index of the BoVW model, reduces overfitting problem on larger sizes of the dictionary and semantic gap issues between high-level image semantic and low-level image features. The qualitative and quantitative analysis conducted on three image benchmarks demonstrates the effectiveness of the proposed approach based on WATH.

A survey on block based copy move image forgery detection techniques

In todays modern life, digital images have significant importance because they have become a leading source of information dissemination. However, the availability of image editing tools made it easier to forge the contents of a digital image; making the authenticity untrustful. Different techniques can be used to forge the digital images. Copy move forgery is the most popular and common approach where a specific part of an image is copied and pasted elsewhere in the same image to conceal unwanted part or object. In this study, we attempted to survey several passive block based copy move forgery detection techniques. A passive technique attempts to identify forgery in digital images without any prior information. A comparison between various techniques is also included.

Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images

The most common image tampering often for malicious purposes is to copy a region of the same image and paste to hide some other region. As both regions usually have same texture properties, therefore, this artifact is invisible for the viewers, and credibility of the image becomes questionable in proof centered applications. Hence, means are required to validate the integrity of the image and identify the tampered regions. Therefore, this study presents an efficient way of copy-move forgery detection (CMFD) through local binary pattern variance (LBPV) over the low approximation components of the stationary wavelets. CMFD technique presented in this paper is applied over the circular regions to address the possible post processing operations in a better way. The proposed technique is evaluated on CoMoFoD and Kodak lossless true color image (KLTCI) datasets in the presence of translation, flipping, blurring, rotation, scaling, color reduction, brightness change and multiple forged regions in an image. The evaluation reveals the prominence of the proposed technique compared to state of the arts. Consequently, the proposed technique can reliably be applied to detect the modified regions and the benefits can be obtained in journalism, law enforcement, judiciary, and other proof critical domains.

An efficient forensic technique for exposing region duplication forgery in digital images

The internet users share a massive amount of digital images daily. The accessibility of powerful image manipulation tools has made the integrity of image contents questionable. The most popular image tampering is to duplicate a region elsewhere in the same image to replicate or conceal some other region. The duplicated regions have identical color and texture attributes that make this artifact invisible to the human eye. Therefore, efficient techniques are required to verify the credibility of image contents by detecting the regions duplicated in the digital images. This paper proposes an efficient technique for exposing region duplication forgery in digital images. The proposed technique divides the approximation (LL) sub-band of shift invariant stationary wavelet transform into overlapping blocks of w × w (i.e. w = 4, 8) sizes. The distinctive features extracted from the overlapping blocks are utilized to expose the region duplication forgeries in digital images. The experimental results of the proposed technique are compared with state-of-the-art techniques that reveal the prominence, and effectiveness of the proposed technique in terms of precision, recall and F1 score for different block sizes. Therefore, the proposed technique can reliably be applied to identify the counterfeited regions and the benefits of the proposed technique can be achieved in different fields for example crime investigation, news reporting, and judiciary.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

Content-based image retrieval (CBIR) is a mechanism that is used to retrieve similar images from an image collection. In this paper, an effective novel technique is introduced to improve the performance of CBIR on the basis of visual words fusion of scale-invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. SIFT performs better on scale changes and on invariant rotations. However, SIFT does not perform better in the case of low contrast and illumination changes within an image, while LIOP performs better in such circumstances. SIFT performs better even at large rotation and scale changes, while LIOP does not perform well in such circumstances. Moreover, SIFT features are invariant to slight distortion as compared to LIOP. The proposed technique is based on the visual words fusion of SIFT and LIOP descriptors which overcomes the aforementioned issues and significantly improves the performance of CBIR. The experimental results of the proposed technique are compared with another proposed novel features fusion technique based on SIFT-LIOP descriptors as well as with the state-of-the-art CBIR techniques. The qualitative and quantitative analysis carried out on three image collections, namely, Corel-A, Corel-B, and Caltech-256, demonstrate the robustness of the proposed technique based on visual words fusion as compared to features fusion and the state-of-the-art CBIR techniques.

Image Enhancement and Segmentation Techniques for Detection of Knee Joint Diseases: A Survey

Knee bone diseases are rare but might be highly destructive. Magnetic resonance imaging (MRI) is the main approach to identify knee cancer and its treatment. Normally, the knee cancers are pointed out with the help of different MRI analysis techniques and latter image analysis strategies understand these images. Computer-based medical image analysis is getting researchers interest due to its advantages of speed and accuracy as compared to traditional techniques. The focus of current research is MRI-based medical image analysis for knee bone disease detection. Accordingly, several approaches for features extraction and segmentation for knee bone cancer are analyzed and compared on benchmark database. Finally, the current state of the art is investigated and future directions are proposed.

Forensic analysis of copy-move forgery in digital images using the stationary wavelets

In this modern life, the digital images are being considered the main source of information sharing. The digital images are being used in various fields such as medical imaging, news reporting, crime investigation, insurance claims etc. However, in the presence of sophisticated image editing tools, the credibility of digital images is the main concern. The copy-move forgery (CMF) is a most popular forgery attack. In CMF, a region is duplicated elsewhere in the same image for producing a forged image. Thus, for detecting this particular artifact an algorithm is presented that utilizes Stationary Wavelet Transform (SWT). The experimental results demonstrate that the algorithm is capable of detecting duplicated blocks precisely and identify multiple CMF effectively, even when the images are contaminated by blurring and noise. The performance of our algorithm in comparison with other systems proves the efficacy of the system with a noticeable increase in detection ratios.