Dipak Kumar Basu

Bio: Dipak Kumar Basu is an academic researcher from Jadavpur University. The author has contributed to research in topics: Facial recognition system & Face (geometry). The author has an hindex of 25, co-authored 136 publications receiving 2379 citations.

A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application

Abstract: Identification of local regions from where optimal discriminating features can be extracted is one of the major tasks in the area of pattern recognition. To locate such regions different kind of region sampling techniques are used in the literature. There is no standard methodology to identify exactly such regions. Here we have proposed a methodology where local regions of varying heights and widths are created dynamically. Genetic algorithm (GA) is then applied on these local regions to sample the optimal set of local regions from where an optimal feature set can be extracted that has the best discriminating features. We have evaluated the proposed methodology on a data set of handwritten Bangla digits. In the present work, we have randomly generated seven sets of local regions and from every set, GA selects an optimal group of local regions which produces best recognition performance with a support vector machine (SVM) based classifier. Other popular optimization techniques like simulated annealing (SA) and hill climbing (HC) have also been evaluated with the same data set and maximum recognition accuracies were found to be 97%, 96.7% and 96.7% for GA, SA and HC, respectively. We have also compared the performance of the present technique with those of other zone based techniques on the same database.

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Abstract: A conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm is presented.The method incorporates conditional affects and spatial information into the membership functions.The algorithm resolves the problem of sensitivity to noise and intensity inhomogeneity in magnetic resonance imaging (MRI) data.The experimental results on four volumes of simulated and one volume of real-patient MRI brain images, each one having 51 images, support efficiency of the csFCM algorithm.The csFCM algorithm has superior performance in terms of qualitative and quantitative studies on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms. The fuzzy C-means (FCM) algorithm has got significant importance due to its unsupervised form of learning and more tolerant to variations and noise as compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorporation of conditioning effects imposed by an auxiliary (conditional) variable corresponding to each pixel, which describes a level of involvement of the pixel in the constructed clusters, and spatial information into the membership functions. The problem of sensitivity to noise and intensity inhomogeneity in magnetic resonance imaging (MRI) data is effectively reduced by incorporating local and global spatial information into a weighted membership function. The experimental results on four volumes of simulated and one volume of real-patient MRI brain images, each one having 51 images, show that the csFCM algorithm has superior performance in terms of qualitative and quantitative studies such as, cluster validity functions, segmentation accuracy, tissue segmentation accuracy and receiver operating characteristic (ROC) curve on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms.

A statistical-topological feature combination for recognition of handwritten numerals

Abstract: Principal Component Analysis (PCA) and Modular PCA (MPCA) are well known statistical methods for recognition of facial images. But only PCA/MPCA is found to be insufficient to achieve high classification accuracy required for handwritten character recognition application. This is due to the shortcomings of those methods to represent certain local morphometric information present in the character patterns. On the other hand Quad-tree based hierarchically derived Longest-Run (QTLR) features, a type of popularly used topological features for character recognition, miss some global statistical information of the characters. In this paper, we have introduced a new combination of PCA/MPCA and QTLR features for OCR of handwritten numerals. The performance of the designed feature-combination is evaluated on handwritten numerals of five popular scripts of Indian sub-continent, viz., Arabic, Bangla, Devanagari, Latin and Telugu with Support Vector Machine (SVM) based classifier. From the results it has been observed that MPCA+QTLR feature combination outperforms PCA+QTLR feature combination and most other conventional features available in the literature.

CMATERdb1: a database of unconstrained handwritten Bangla and Bangla–English mixed script document image

Abstract: In this paper, we have described the preparation of a benchmark database for research on off-line Optical Character Recognition (OCR) of document images of handwritten Bangla text and Bangla text mixed with English words. This is the first handwritten database in this area, as mentioned above, available as an open source document. As India is a multi-lingual country and has a colonial past, so multi-script document pages are very much common. The database contains 150 handwritten document pages, among which 100 pages are written purely in Bangla script and rests of the 50 pages are written in Bangla text mixed with English words. This database for off-line-handwritten scripts is collected from different data sources. After collecting the document pages, all the documents have been preprocessed and distributed into two groups, i.e., CMATERdb1.1.1, containing document pages written in Bangla script only, and CMATERdb1.2.1, containing document pages written in Bangla text mixed with English words. Finally, we have also provided the useful ground truth images for the line segmentation purpose. To generate the ground truth images, we have first labeled each line in a document page automatically by applying one of our previously developed line extraction techniques [Khandelwal et al., PReMI 2009, pp. 369–374] and then corrected any possible error by using our developed tool GT Gen 1.1. Line extraction accuracies of 90.6 and 92.38% are achieved on the two databases, respectively, using our algorithm. Both the databases along with the ground truth annotations and the ground truth generating tool are available freely at http://code.google.com/p/cmaterdb.

Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition

Abstract: Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characters After preprocessing the character image, we extracted shadow features, chain code histogram features, view based features and longest run features These features are then fed to Neural classifier and in support vector machine for classification In neural classifier, we explored three ways of combining decisions of four MLP’s, designed for four different features

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

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.

Artificial Intelligence in Healthcare

Abstract: Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.