Malay K. Kundu

Bio: Malay K. Kundu is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Image retrieval & Digital watermarking. The author has an hindex of 33, co-authored 151 publications receiving 3283 citations. Previous affiliations of Malay K. Kundu include Intel.

A Study on Partitioned Iterative Function Systems for Image Compression

Abstract: The technique of image compression using Iterative Function System (IFS) is known as fractal image compression. An extension of IFS theory is called as Partitioned or local Iterative Function System (PIFS) for coding the gray level images. The theory of PIFS appears to be different from that of IFS in the sense of application domain. Assuming the theory of PIFS is the same as that of IFS, several techniques of image compression have been developed. In the present article we have studied the PIFS scheme as a separate one and proposed a mathematical formulation for the existence of its attractor. Moreover the results of a Genetic Algorithm (GA) based PIFS technique [1] is presented. This technique appears to be efficient in the sense of computational cost.

A Ripplet Transform Based Statistical Framework for Natural Color Image Retrieval

Abstract: We present a novel Content Based Image Retrieval (CBIR) scheme for natural color images using Multi-scale Geometric Analysis (MGA) of Ripplet Transform (RT) Type-I in the statistical framework based on Generalized Gaussian Density (GGD) model and Kullback-Leibler Distance (KLD). The system is based on modeling the marginal distributions of RT coefficients by GGD framework and computing the similarity between the model parameters using the KLD. Least Square-Support Vector Machine (LS-SVM) classifier is used to classify the images of the database. Extensive experiments were carried out to evaluate the effectiveness of the proposed system on two image databases consisting 1000 (Simplicity) and 2788 (Oliva) images, respectively. Experimental results and comparisons show that the proposed CBIR system performs efficiently in image retrieval field.

Compact image signature generation: An application in image retrieval

Abstract: In this article, we have proposed a novel Content Based Image Retrieval (CBIR) system, where each image in the database is represented by a compact image signature which is computed using the Nonsubsampled Contourlet Transform (NSCT) and Fuzzy-C-means (FCM) technique. To improve the retrieval accuracy, the proposed system incorporates Least Square Support Vector Machine (LS-SVM) based classifier, Earth Mover's Distance (EMD) and Relevance Feedback Mechanism (RFM). Extensive experiments were carried out to evaluate the effectiveness of the proposed system on SIMPLIcity image database consisting of 1000 images. Experimental results and comparisons show that the proposed CBIR system performs efficiently in image retrieval domain.

Robust digital image watermarking utilizing a Walsh transform algorithm

Abstract: Disclosed is a method, apparatus, and system to provide for robust digital image watermarking utilizing a Walsh transform algorithm. A first image processor performs operations including partitioning a cover image, generating a key, and inserting a watermark symbol into the cover image utilizing a Walsh transform and the key. A second image processor performs operations including extracting the watermark symbol from the cover image utilizing a Walsh transform and the key.

Robust and low cost watermarking using image characteristics

Abstract: Most of the digital image watermarking techniques use pixel values, frequency or other transform coefficients to embed information without considering the perceptually significant portion of the cover. The present work selects the perceptually significant region of the cover and embeds data in the transform coefficients in order to design low-cost robust watermarking scheme. Experimental results using several benchmark image samples are reported.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

IEEE transactions on pattern analysis and machine intelligence

Abstract: 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.