International Journal of Computer and Applications

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Fast Fourier Transform

Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.

Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems' behavior.

Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction

In this paper we propose segmentation approach based on Vector Quantization technique. Here we have used Kekre’s fast codebook generation algorithm for segmenting low-altitude aerial image. This is used as a preprocessing step to form segmented homogeneous regions. Further to merge adjacent regions color similarity and volume difference criteria is used. Experiments performed with real aerial images of varied nature demonstrate that this approach does not result in over segmentation or under segmentation. The vector quantization seems to give far better results as compared to conventional on-the-fly watershed algorithm. Keywords—Image Segmentation,, Codebook, Codevector, data compression, Encoding

/pdf/color-image-segmentation-using-kekre-s-algorithm-for-vector-497i0gzo4g.pdf

Color Image Segmentation Using Kekre-s Algorithm for Vector Quantization

Technological advances in digital imaging, broadband networking, and data storage have motivated people to communicate and express by sharing images, video, and other forms of media online [1]. Although the problems of acquiring, storing and transmitting the images are well addressed, capabilities to manipulate, index, sort, filter, summarize, or search through image database lack maturity. Modern image search engines [1–3, 30] retrieve the images based on their visual contents, commonly referred to as Content Based Image Retrieval (CBIR) systems. CBIR systems have found applications in various fields like fabric and fashion design, interior design as panoramic views [5, 20, 38–41], art galleries [22], museums, architecture/ engineering design [22], weather forecast, geographical information systems, remote sensing and management of earth resources [40, 41], scientific database management, medical imaging, trademark and copyright database management, the military, law enforcement and criminal investigations [41], intellectual property, picture archiving and communication systems, retailing and image search on the Internet (very general image content).

Improved texture feature based image retrieval using Kekre’s fast codebook generation algorithm

Inception of Hybrid Wavelet Transform using Two Orthogonal Transforms and It’s use for Image Compression

This paper presents a very simple and efficient algorithm for codebook search, which reduces a great deal of computation as compared to the full codebook search. The algorithm is based on sorting and centroid technique for search. The results table shows the effectiveness of the proposed algorithm in terms of computational complexity. In this paper we also introduce a new performance parameter named as Average fractional change in pixel value as we feel that it gives better understanding of the closeness of the image since it is related to the perception. This new performance parameter takes into consideration the average fractional change in each pixel value. Keywords—Vector Quantization, Data Compression, Encoding, Searching.

Fast Codevector Search Algorithm for 3-D Vector Quantized Codebook

In this paper we introduce segmentation approach which uses fast codebook generation algorithm based on energy ordering concept It is specifically designed to segment low-altitude aerial images which can be used as a preprocessing step to 3D reconstruction This approach uses color similarity and volume difference criteria to merge adjacent regions Experiments performed with real aerial images of varied nature demonstrate that this approach does not result in over segmentation or under segmentation allowing large-scale urban scenes to be segmented in an accurate, reliable and fully automatic way The vector quantization seems to give far better results as compared to conventional on-the-fly watershed algorithm

Tanuja Sarode

Papers

Color Image Segmentation Using Kekre-s Algorithm for Vector Quantization

Improved texture feature based image retrieval using Kekre’s fast codebook generation algorithm

Inception of Hybrid Wavelet Transform using Two Orthogonal Transforms and It’s use for Image Compression

Fast Codevector Search Algorithm for 3-D Vector Quantized Codebook

Color image segmentation using Kekre's fast codebook generation algorithm based on energy ordering concept