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 hybrid data and space partitioning technique for similarity queries on bounded clusters

Abstract: In this paper, a new method for generating size-bounded clusters is proposed such that the cardinality of each cluster is less than or equal to a pre-specified value. First, set estimation techniques coupled with Rectangular Intersection Graphs are used to generate adaptive clusters. Then, the size-bounded clusters are obtained by using space partitioning techniques. The clusters can be indexed by a Kd-tree like structure for similarity queries. The proposed method is likely to find applications to Content Based Image Retrieval (CBIR).

Nondestructive Testing Image Segmentation based on Neutrosophic Set and Bat Algorithm

Abstract: Industry uses nondestructive testing (NDT) to detect a fault in metal without damaging it. Image segmentation based technique for detecting the fault from an NDT image is a difficult task. The difficulty emerges due to uncertainties in the NDT image pattern. To segment an NDT image efficiently the uncertainties should be handled efficiently. In this paper, we present a novel technique to segment an NDT image by handling the uncertainties based on neutrosophic set(NS). The NS manages the uncertainties by representing an image into a true, false, and indeterminate subset. For proper NS value representation, two operations α – mean and β – enhancement are essential. For finding the proper values of α and β depending on the image statistics we utilize the bat algorithm(BA). The algorithm finds the optimal values of α and β for managing the uncertainties properly. We find that in terms of performance the proposed method is quite satisfying in comparison to the latest methods.

Rough-Fuzzy Clustering and M-Band Wavelet Packet for Text-Graphics Segmentation

Abstract: This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics regions. It assumes that the text and non-text regions of a given document are considered to have different textural properties. The M-band wavelet packet is used to extract the scale-space features, which is able to zoom it onto narrow band high frequency components of a signal. A scale-space feature vector is thus derived, taken at different scales for each pixel in an image. Finally, the rough-fuzzy-possibilistic c-means algorithm is used to address the uncertainty problem of document segmentation. The performance of the proposed technique, along with a comparison with related approaches, is demonstrated on a set of real life document images.

The Comparative Study of a New Pattern Recognition Based Exact Coding Method with Conventional Coding Methods for Two Tone Graphics

Abstract: A new Pattern Recognition (P R) based coding method for two tone contour type of graphics is introduced in this paper. The basic P R concept used here is the detection of a digital line segment in accordance with the Freeman's definition of digital line. The relative data compressibility of the new method is compared with that of different widely used representative coding methods. The relative performances of all methods are studied both on normal and noisily degraded data. The effect of gradual increase of noise level on data compressibility is studied for all the methods. The extension of the new methods is also proposed.

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.