S
Santanu Chaudhury
Researcher at Indian Institute of Technology, Jodhpur
Publications - 389
Citations - 4361
Santanu Chaudhury is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Ontology (information science) & Deep learning. 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.
Papers
More filters
Journal ArticleDOI
Hybrid MPSO-CNN: Multi-level Particle Swarm optimized hyperparameters of Convolutional Neural Network
TL;DR: An approach to suggest the best well-conditioned CNN architecture and its hyperparameters using MPSO in a specified search space is explored and the experimental results on 5 benchmark datasets have demonstrated one more effective application of PSO in learning a deep neural architecture.
Journal ArticleDOI
Nrityakosha: Preserving the intangible heritage of Indian classical dance
TL;DR: The efficacy of the ontology-based approach is demonstrated by constructing an ontology for the cultural heritage domain of Indian classical dance, and a browsing application is developed for semantic access to the heritage collection of Indian dance videos.
Proceedings ArticleDOI
Biometrics based Asymmetric Cryptosystem Design Using Modified Fuzzy Vault Scheme
Abhishek Nagar,Santanu Chaudhury +1 more
TL;DR: The use of invariant features as a key to producing a hierarchical security system where the same key (fingerprint) can be used to generate encrypted messages at different levels of security.
Proceedings ArticleDOI
Text recognition using deep BLSTM networks
TL;DR: A Deep Bidirectional Long Short Term Memory (LSTM) based Recurrent Neural Network architecture for text recognition that uses Connectionist Temporal Classification (CTC) for training to learn the labels of an unsegmented sequence with unknown alignment.
Patent
Advance video coding with perceptual quality scalability for regions of interest
TL;DR: In this paper, a video compression framework based on parametric object and background compression is proposed, where an object is detected and frames are segmented into regions corresponding to the foreground object and the background.