Santanu Chaudhury

Bio: Santanu Chaudhury is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Ontology (information science) & Image segmentation. The author has an hindex of 28, co-authored 380 publications receiving 3691 citations. Previous affiliations of Santanu Chaudhury include Central Electronics Engineering Research Institute & Indian Institute of Technology Delhi.

Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept Drift

Abstract: Data abundance along with scarcity of machine learning experts and domain specialists necessitates progressive automation of end-to-end machine learning workflows. To this end, Automated Machine Learning (AutoML) has emerged as a prominent research area. Real world data often arrives as streams or batches, and data distribution evolves over time causing concept drift. Models need to handle data that is not independent and identically distributed (iid), and transfer knowledge across time through continuous self-evaluation and adaptation adhering to resource constraints. Creating autonomous self-maintaining models which not only discover an optimal pipeline, but also automatically adapt to concept drift to operate in a lifelong learning setting was the crux of NeurIPS 2018 AutoML challenge. We describe our winning solution to the challenge, entitled AutoGBT, which combines an adaptive self-optimized end-to-end machine learning pipeline based on gradient boosting trees with automatic hyper-parameter tuning using Sequential Model-Based Optimization (SMBO). We report experimental results on the challenge datasets as well as several benchmark datasets affected by concept drift and compare it with the baseline model for the challenge and Auto-sklearn. Results indicate the effectiveness of the proposed methodology in this context.

Deep learning based frameworks for image super-resolution and noise-resilient super-resolution

Abstract: Our paper is motivated from the advancement in deep learning algorithms for various computer vision problems. We are proposing a novel end-to-end deep learning based framework for image super-resolution. This framework simultaneously calculates the convolutional features of low-resolution (LR) and high-resolution (HR) image patches and learns the non-linear function that maps these convolutional features of LR image patches to their corresponding HR image patches convolutional features. Here, proposed deep learning based image super-resolution architecture is termed as coupled deep convolutional auto-encoder (CDCA) which provides state-of-the-art results. Super-resolution of a noisy/distorted LR images results in noisy/distorted HR images, as super-resolution process gives rise to spatial correlation in the noise, and further, it cannot be de-noised successfully. Traditional noise resilient image super-resolution methods utilize a de-noising algorithm prior to super-resolution but de-noising process gives rise to loss of some high-frequency information (edges and texture details) and super-resolution of the resultant image provides HR image with missing edges and texture information. We are also proposing a novel end-to-end deep learning based framework to obtain noise resilient image super-resolution. Proposed end-to-end deep learning based framework for noise resilient super-resolution simultaneously perform image de-noising and super-resolution as well as preserves textural details. First, stacked sparse de-noising auto-encoder (SSDA) was learned for LR image de-noising and proposed CDCA was learned for image superresolution. Then, both image de-noising and super-resolution networks were cascaded. This cascaded deep learning network was employed as one integral network where pre-trained weights were serving as initial weights. The integral network was end-to-end trained or fine-tuned on a database having noisy, LR image as an input and target as an HR image. In fine-tuning, all layers of the combined end-to-end network was jointly optimized to perform image de-noising and super-resolution simultaneously. Experimental results show that proposed noise resilient super-resolution framework outperforms the conventional and state-of-the-art approaches in terms of PSNR and SSIM metrics.

Enhancement of Old Manuscript Images

Abstract: In this paper we address the issue of enhancement in the quality of scanned images of old manuscripts. Small portions of the text in these manuscripts have degraded with time and are not readable. We propose a segmentation based histogram matching scheme for enhancing these degraded text regions. To automatically identify the degraded text we use a matched wavelet based text extraction algorithm followed by MRF(Markov Random Field) post processing. Additionally we do background clearing to improve the quality of results. This method does not require any a priori information about the font, font size, background texture or geometric transformation. We have tested our method on a variety of manuscript images. The results show proposed method to be a robust, versatile and effective tool for enhancement of manuscript images.

Data clustering: a review

Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Online and off-line handwriting recognition: a comprehensive survey

Abstract: Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.