Prof.Vipin Tyagi

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Content-Based Image Retrieval

Retrieval performance of a content-based image retrieval system is affected by similarity measure... more Retrieval performance of a content-based image retrieval system is affected by similarity measures used in the development of the system. Similarity measures indicate that how two images are matching to each other. Several similarity measures for retrieval have been developed by various researchers. In this chapter, some commonly used similarity measures are described. After development of a retrieval system, it is necessary to check performance of the system in terms of output generated in response to a query, in comparison to other state-of-the-art systems. This chapter also describes some common measures that are used to evaluate the performance on CBIR systems.

Content-Based Image Retrieval Using Integrated Color, Texture, and Shape Features

Content-Based Image Retrieval

In this chapter, a content-based image retrieval technique based on the concept of region-based i... more In this chapter, a content-based image retrieval technique based on the concept of region-based image retrieval has been described. This technique integrates color, texture, and shape features using local binary patterns (LBPs). In this technique, the image is divided into a fixed number of blocks and from each block LBP-based color, texture, and shape features are computed. The color and texture features are extracted using LBP histograms of quantized color image and gray-level images, respectively. Shape features are computed using the binary edge map obtained using Sobel edge detector from each block. All three features are combined to make a single completed binary region descriptor (CBRD) represented in the LBP way. To support region-based retrieval, an effective region code-based scheme is employed. In this technique, the spatial relative locations of objects are also considered to increase the retrieval accuracy.

Problem Reduction in Online Payment System Using Hybrid Model

Online auction, shopping, electronic billing etc. all such types of application involves problems... more Online auction, shopping, electronic billing etc. all such types of application involves problems of fraudulent transactions. Online fraud occurrence and its detection is one of the challenging fields for web development and online phantom transaction. As no-secure specification of online frauds is in research database, so the techniques to evaluate and stop them are also in study. We are providing an approach with Hidden Markov Model (HMM) and mobile implicit authentication to find whether the user interacting online is a fraud or not. We propose a model based on these approaches to counter the occurred fraud and prevent the loss of the customer. Our technique is more parameterized than traditional approaches and so,chances of detecting legitimate user as a fraud will reduce.

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Content-Based Image Retrieval: An Introduction

Content-Based Image Retrieval

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Content-Based Image Retrieval

This chapter provides an introduction to information retrieval and image retrieval. Types of imag... more This chapter provides an introduction to information retrieval and image retrieval. Types of image retrieval techniques, i.e., text-based image retrieval and content-based image retrieval techniques are introduced. A brief introduction to visual features like color, texture, and shape is provided. Similarity measures used in content-based image retrieval and performance evaluation of content-based image retrieval techniques are also given. Importance of user interaction in retrieval systems is also discussed.

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Mathematical Morphology

Understanding Digital Image Processing

Mathematical Tools for Image Processing

Methods to Distinguish Photorealistic Computer Generated Images from Photographic Images: A Review

Communications in Computer and Information Science

Uses of digital images have increased multifold in last few years in various important fields suc... more Uses of digital images have increased multifold in last few years in various important fields such as virtual reality, gaming, social media, magazine, news papers, medical, legal issues, law, academics etc. At the same time, image editing and rendering tools have also evolved significantly. With the help of computers and such advanced image rendering tools it is possible to create photorealistic computer graphics images effortlessly. It is very difficult to discriminate such photorealistic computer graphics images from actual photographic images taken from digital cameras by human visual system. If computer generated images are used with malicious intentions it creates negative impact on the society. Therefore, several methods have been proposed in last few years to distinguish computer generated images from photographic images. This paper presents a comprehensive review of the existing methods. A classification of all existing methods is also provided based on the use of feature extraction techniques and classifier used. Accordingly, all the existing methods are grouped into four categories: statistical feature based, acquisition process based, visual feature based, and hybrid feature based methods. This paper also reviews publically available related image datasets and suggests the future directions.

Distinguishing computer-generated images from photographic images using two-stream convolutional neural network

Applied Soft Computing

Abstract Today’s advanced multimedia tools allow us to create photorealistic computer graphic ima... more Abstract Today’s advanced multimedia tools allow us to create photorealistic computer graphic images, effortlessly. There are various fields such as the film industry, virtual reality, video games where computer-generated (CG) images are used widely. CG images can also be misused in many ways. Therefore, there is a need of distinguishing CG images from real photographic (PG) images. This paper proposes a method to distinguish CG images from PG images using a two-stream convolutional neural network (CNN). In the proposed method, the first stream takes the advantage of the knowledge learned by the pre-trained VGG-19 network, and then this knowledge is transferred to learn the distinct features of CG and PG images. Here, we propose a second stream, that preprocesses the images using three high-pass filters which aim to help the network to focus on noise-based distinct features of CG and PG images. Finally, we propose an ensemble model to merge the outcomes of the proposed two streams. Comparative and self-analysis experiments demonstrates that the proposed method outperforms the state-of-the-art methods in terms of classification accuracy. The experimental results also show that the proposed method performs satisfactorily under the additive white Gaussian noise postprocessing operation in the images.

Texture Image Retrieval Based on Block Level Directional Local Extrema Patterns Using Tetrolet Transform

Communications in Computer and Information Science

This paper introduces a novel texture image retrieval technique based on block level processing u... more This paper introduces a novel texture image retrieval technique based on block level processing using Tetrolet and optimized directional local extrema patterns. Texture image categorization is performed for uniform and non-uniform distribution of the intensities within the image. Texture features are extracted by using Tetrolet transform and directional local extrema pattern. Image is processed at block level for extracting these features. The main concept of this approach is to analyze the image at block level to get better results in retrieval process. During image search, each block is compared with the corresponding block of another image. Categorization of the images reduces the search space. Proposed approach uses spatial and spectral domain analysis of the image. Performance of proposed retrieval system is tested on the Brodatz and VisTex benchmark databases. Retrieval results show that the proposed technique performs better in terms of average retrieval rate in comparison to other state-of-the-art techniques.

A Deep Learning based Method for Image Splicing Detection

Journal of Physics: Conference Series

Image manipulation has become an easy task due to the availability of user-friendly multimedia to... more Image manipulation has become an easy task due to the availability of user-friendly multimedia tools. The images can be manipulated in several ways. Image splicing is one of such image manipulation methods in which two or more images are merged to obtain a single composite image. These manipulated images can be misused to cheat others. This paper proposes a deep learning-based method to detect image splicing in the images. First, the input image is preprocessed using a technique called ‘Noiseprint’ to get the noise residual by suppressing the image content. Second, the popular ResNet-50 network is used as a feature extractor. Finally, the obtained features are classified as spliced or authentic using the SVM classifier. The experiments performed on the CUISDE dataset show that the proposed method outperforms other existing methods. The proposed method achieves an average classification accuracy of 97.24%.

A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms

Multimedia Tools and Applications

In digital images, the most common forgery is copy-move image forgery in which some region(s) of ... more In digital images, the most common forgery is copy-move image forgery in which some region(s) of an image is replicated within the image. The copy-move forgery detection (CMFD) techniques fall under two categories; keypoint-based and block-based. The keypoint-based techniques perform well under rotation and scaling but show very poor performance in the case of smooth images. On the contrary, the block-based techniques perform better in smooth images but are comparatively more time demanding. In this paper, a hybrid technique has been proposed by combining the block-based technique using Fourier-Mellin Transform (FMT) and a keypoint-based technique using Scale Invariant Feature Transform (SIFT). In this technique, the input image to be checked for forgery is first divided into texture and smooth regions. Then the keypoints are extracted from the texture part of the image using the SIFT descriptor, and the FMT is applied on the smooth part of the image. Extracted features are then matched to detect the duplicated regions of the image. The experimental results illustrate that the proposed technique performs better in comparison to other state-of-the-art CMFD techniques under various geometric transformations and post-processing operations in reasonable time.

A copy-move image forgery detection technique based on tetrolet transform

Journal of Information Security and Applications

Abstract Copy-move forgery is a common type of forgery in digital images. In copy-move forgery, o... more Abstract Copy-move forgery is a common type of forgery in digital images. In copy-move forgery, one part of the image is replicated within the same image, generally at different location. For revival of trustworthiness of images, there is a need to develop an efficient and robust technique to detect such forgeries. This paper proposes a new copy-move image forgery detection technique based on Tetrolet transform. In this technique, initially the input image is divided into overlapping blocks, then four low-pass coefficients and twelve high-pass coefficients are extracted from each block by applying Tetrolet transform. Feature vectors are then sorted lexicographically, and similar blocks are identified by matching the extracted Tetrolet features. Experimental results show that the proposed technique can detect and locate the duplicated regions in the images very accurately, even when the copied regions have undergone some post-processing operations blurring, color reduction, adjustment of brightness and contrast, rotation, scaling, JPEG compression. In addition, it is also observed that the proposed technique is able to detect very small duplicated regions and multiple forgery cases, even when image is smooth.

A copy-move image forgery detection technique based on Gaussian-Hermite moments

Multimedia Tools and Applications

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Predictive Computing and Information Security

A novel technique for content based image retrieval based on region-weight assignment

Multimedia Tools and Applications

This paper presents a novel technique for content based image retrieval (CBIR) that selects and a... more This paper presents a novel technique for content based image retrieval (CBIR) that selects and assigns weights to the regions of the image on the basis of their contribution to image contents, using a new region-weight assignment scheme. Assigning the weight to each region ignores the irrelevant regions of the image during retrieval and thus maximizes the retrieval accuracy. The proposed approach performs the feature extraction at both region-level and image-level. Texture and edge features are extracted at region-level whereas shape feature is extracted at image-level. At region-level, the image is divided into non-overlapping regions and texture and edge features are calculated for each region separately. Curvelet transform is used for extracting the texture feature using the curve continuity as well as line continuity in the feature extraction process. Moment invariant is used for extracting the shape features. Integrated Region Matching (IRM) technique is used for retrieving the relevant images. The proposed approach does the best use of the features by balancing the regions and features in the similarity matching of the regions. The performance of the proposed technique is tested on COREL and CIFAR databases. Experimental results show the effectiveness of proposed region weight assignment scheme over the feature weight assignment scheme in image retrieval in comparison to other state-of-the-art techniques.

Feed-forward content based image retrieval using adaptive tetrolet transforms

Multimedia Tools and Applications

This paper proposes a new approach for content based image retrieval based on feed-forward archit... more This paper proposes a new approach for content based image retrieval based on feed-forward architecture and Tetrolet transforms. The proposed method addresses the problems of accuracy and retrieval time of the retrieval system. The proposed retrieval system works in two phases: feature extraction and retrieval. The feature extraction phase extracts the texture, edge and color features in a sequence. The texture features are extracted using Tetrolet transform. This transform provides better texture analysis by considering the local geometry of the image. Edge orientation histogram is used for retrieving the edge feature while color histogram is used for extracting the color features. Further retrieval phase retrieves the images in the feed-forward manner. At each stage, the number of images for next stage is reduced by filtering out irrelevant images. The Euclidean distance is used to measure the distance between the query and database images at each stage. The experimental results on COREL- 1 K and CIFAR - 10 benchmark databases show that the proposed system performs better in terms of the accuracy and retrieval time in comparison to the state-of-the-art methods.

Content-Based Image Retrieval Techniques: A Review

Content-Based Image Retrieval

In recent years, a rapid increase in the size of digital image databases has been observed. Every... more In recent years, a rapid increase in the size of digital image databases has been observed. Everyday gigabytes of images are generated. Consequently, the search for the relevant information from image and video databases has become more challenging. To get accurate retrieval results is still an unsolved problem and an active research area. Content-based image retrieval (CBIR) is a process in which for a given query image, similar images are retrieved from a large image database based on their content similarity. A number of techniques have been suggested by researchers for content-based image retrieval. In this chapter, a review of some state-of-the-art retrieval techniques is provided.

Corrigendum to ``Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching'' [Information Sciences 259 (2014) 212–224]

Information Sciences

Dynamic texture recognition using multiresolution edge-weighted local structure pattern

Computers & Electrical Engineering, 2016

Abstract Dynamic texture has been found as a powerful cue for modeling natural scenes such as fir... more Abstract Dynamic texture has been found as a powerful cue for modeling natural scenes such as fire, waves and smoke, etc. It combines appearance with motion to characterize the moving scene that exhibits certain spatially repetitive and time-varying visual patterns. This paper proposes a new method of recognizing dynamic texture using the well-known texture descriptor, local binary pattern. The new variant differentiates different structural patterns more efficiently using the additional information from the local patch. This pattern information is further combined with shape information to improve the discriminative power of texture descriptor. The proposed method is extended to multiscale using classifier fusion scheme to capture the spatio-temporal content of a moving scene at multiple scales, thus improves representation capability of the new descriptor. Proposed descriptor is tested on three dynamic texture databases: UCLA, Dyntex and Dyntex++. Results demonstrate that the proposed feature descriptor outperforms various state-of-the-art approaches on all representative databases in terms of classification accuracy.