Dr. Ajay Kumar Mallick

@nith.ac.in

Assistant Professor, Department of Computer Science and Engineering
National Institute of Technology, Hamirpur

Dr. Ajay Kumar Mallick


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https://researchid.co/ajaymallick
Ajay Kumar Mallick completed his B.E in Computer Science and Engineering from the University Institute of Technology, which is affiliated to the University of Burdwan and MTech in Computer Science and Engineering (CSE) from Indian Institute of Technology (Indian School of Mines), Dhanbad, India in 2013 and 2015, respectively. He completed his Ph.D. degree from the Department of CSE, Indian Institute of Technology (Indian School of Mines), Dhanbad, India. Presently, he works as Assistant Professor in the Department of CSE, National Institute of Technology, Hamirpur, India. He possesses membership in many technical professional organizations such as IEEE Membership (student) and life membership to the Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI). His research area and interest include content based video retrieval, image processing, and image watermarking, machine learning. He has published articles in many internationally reputed Journals and conferences.

EDUCATION

B.E – University Institute of Technology, Burdwan, India
MTech - Indian Institute of Technology (Indian School of Mines), Dhanbad, India
PhD - Indian Institute of Technology (Indian School of Mines), Dhanbad, India

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Artificial Intelligence, Computer Engineering
15

Scopus Publications

69

Scholar Citations

5

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • A comprehensive survey of content based image retrieval schemes: advancements, challenges, and future directions
    Mukul Majhi, Ajay Kumar Mallick
    Multimedia Tools and Applications, 2026
  • Fusion of Handcrafted and Deep Convolution Networks Learned Features for Image Retrieval
    Sourabh Sharma, Ajay Kumar Mallick
    6th IEEE International Conference on Recent Advances in Information Technology Rait 2025, 2025
    The rapid increase in online services such as image sharing has led to the accumulation of unprecedented amounts of multimedia entities in repositories. Consequently, large amount of raw image data need to be analyzed for content and semantics. In this context, Content-Based Image Retrieval (CBIR) has become a crucial task in computer vision, focusing on retrieving images from large datasets based on the corresponding visual content rather than metadata. In this research, we propose a hybrid CBIR framework that integrate handcrafted and convolutional feature extraction and matching. The features extracted include Color Histogram, Oriented FAST and Rotated BRIEF (ORB), and deep features derived from a pre-trained ResNet-50 convolutional neural network. Handcrafted features capture information related to local, global, and computationally efficient keypoint descriptors, providing valuable insights into visual data, while deep features contribute discriminative properties for robust image representation. We fuse these complementary features to create a comprehensive image representation, enhancing retrieval accuracy and nominal retrieval time. Experimental analysis on benchmark datasets imply that the proposed approach satisfactorily outperforms traditional CBIR methods, achieving 96.6% high precision, effective recall and nominal retrieval time. Thus, the work highlights the efficacy of combining handcrafted and deep learning features for robust image retrieval applications.
  • AttentiveFP: An Attention-Guided Deep Learning Approach for Fingerprint Liveness Detection
    Atul Sharma, Ajay Kumar Mallick
    2025 IEEE International Conference on Computer Electronics Electrical Engineering and their Applications Ic2e3 2025, 2025
    Biometric security systems face an increasing threat from artificial fingerprint spoofing. This research explores the effectiveness of attention-based techniques in differentiating genuine fingerprints from counterfeit ones. We introduce AttentiveFP, a deep learning approach that utilizes a dual-stream framework to analyze both holistic fingerprint patterns and localized discriminative details. By incorporating spatial attention mapping and targeted feature refinement, our method enhances the detection of subtle differences between real and fake fingerprints. Evaluations of the LivDet-2015 dataset demonstrate that our approach achieves outstanding accuracy with Cross-Match sensors (ACER of 1.77%), while maintaining robust performance across various sensor types. The model is designed to optimize detection accuracy while managing computational efficiency through adaptive thresholding, making it well suited for real-world applications. Our experimental findings confirm that Attentive-FP effectively distinguishes live fingerprints from artificial imitations while ensuring efficient processing, addressing critical concerns in practical biometric authentication systems.
  • Fusion of Deep Cross Block Stage and YOLOv9 for Enhanced Small Object Detection in Aerial Imagery
    Sameer Mirza, Ajay Kumar Mallick
    2025 IEEE International Conference on Computer Electronics Electrical Engineering and their Applications Ic2e3 2025, 2025
    Small object detection is used extensively in real life and is a key component of computer vision. However, because of their poor resolution and ambiguous features, small objects are frequently missed, lowering the detection accuracy. To tackle the problem, this paper presents a deep learning framework for small object detection in aerial imagery, focusing on small object detection challenges with major emphasis on vehicle detection. The proposed framework integrates Cross Block Stage modules with YOLOv9, enhancing feature representation and multi-scale processing capabilities. Experimental evaluation on the VEDAI dataset demonstrates significant performance improvements, achieving 0.873 accuracy compared to current architectures including FFCA-YOLO (0.748), YOLOv5m (0.723), and YOLOv8m (0.686). The framework exhibits strong category-specific performance, with car and truck detection accuracies of 0.926 and 0.820 respectively, establishing its effectiveness for aerial vehicle detection applications.
  • Revolutionizing Tomato Agriculture: Leaf Disease Detection Using CNN and Its Variants
    Malika Sood, Jyoti Srivastava, Ajay Kumar Mallick
    2024 IEEE 9th International Conference for Convergence in Technology I2ct 2024, 2024
    India, being a large agricultural market is considered to be one of the major producers of tomatoes in the world having high economic value. However, the volume and quality of tomato crop output are diminishing day by day owing to several factors that impact the crop productivity resulting in significant losses for the farmers. The escalating challenges in tomato agriculture, therefore, demand innovative solutions for the timely and accurate identification of plant diseases. Numerous works have been provided in the literature to address these problems, however attaining high accuracy is still a major challenge. To tackle these inadequacies, we proposed an analytical approach based on Convolutional Neural Networks with a major emphasis on ResNet50 and VGG16 architectures to enable better complex plant disease pattern detection. For our experiment analysis and evaluation, we exploited a labeled dataset obtained from Kaggle comprising 10,388 images with 10 different tomato leaf disease classes. Our experiment illustrated satisfactory plant detection accuracy. It also outperforms some of the methods in the literature and attains 99.63% and 94.48% training accuracy at 20 epochs for ResNet50 and VGG16 models respectively. The enhanced result of our approach is evident from the knowledge of transfer learning that imports pre-trained Resnet50 and VGG16 models with several data augmentation techniques.
  • Random projection and hashing based privacy preserving for image retrieval paradigm using invariant and clustered feature
    Mukul Majhi, Ajay Kumar Mallick
    Journal of King Saud University Computer and Information Sciences, 2022
    In recent years, exploration of local characteristics have been an effective feature extraction technique for managing multimedia repository for image retrieval. These class of approaches accelerate the image retrieval. However, with the increase of online intrusion, it is also essential to provide confidentiality for the private online data with effective functionality and significant retrieval accuracy. In this paper, we have solved the protuberant bottleneck problem of privacy-preserving in CBIR using random projection technique for feature encryption with effective retrieval. For this, we formulated highly discriminative interest points based features that are rotation and translation invariant and are constructed using k-means clustering and silhouette concept. Moreover, we have devised locally likely arrangement hashing technique that can incorporate fast, accurate, and secure matching over encrypted features. Based on the experiment, it substantially outperforms the recent state-of-the-art techniques in terms of retrieval accuracy and time overhead on various standard benchmarks, namely, Corel, Olivia, ALOI, and MPEG-7. The proposed method on average provided retrieval accuracy of 83.10%, 81.30%, 81.62%, 73.01% for Corel, Olivia, ALOI and MPEG-7 plaintexted feature, respectively while retrieval accuracy of 79.90% for Corel, 79.10% for Olivia, 79.97% for ALOI, and 67.34% for MPEG-7 using the encrypted feature.
  • Video retrieval framework based on color co-occurrence feature of adaptive low rank extracted keyframes and graph pattern matching
    Ajay Kumar Mallick, Susanta Mukhopadhyay
    Information Processing and Management, 2022
  • Cathedral and indian mughal monument recognition using tensorflow
    Aniket Ninawe, Ajay Kumar Mallick, Vikash Yadav, Hifzan Ahmad, Dinesh Kumar Sah, Cornel Barna
    Advances in Intelligent Systems and Computing, 2021
  • Video retrieval using salient foreground region of motion vector based extracted keyframes and spatial pyramid matching
    Ajay Kumar Mallick, Susanta Mukhopadhyay
    Multimedia Tools and Applications, 2020
  • Video Retrieval Based on Motion Vector Key Frame Extraction and Spatial Pyramid Matching
    Ajay Kumar Mallick, Sushanta Mukhopadhyay
    2019 6th International Conference on Signal Processing and Integrated Networks Spin 2019, 2019
    Video retrieval technique aims at retrieving relevant video from a large video dataset. Generally, it consists of heterogeneous videos and retrieval of relevant videos with high accuracy is a tedious work. In this paper, we proposed a content-based video retrieval system that correspondingly retrieves the most relevant videos from the database based on the visual content. In this context, motion vector based key frame extraction is computed as video summarization technique to recapitulate the video content and spatial pyramid matching scheme is used for the key frame matching. Computational mechanism for key frame selection is based on the concept of shot selection using outliers detection followed by sub-shot detection for each shot using motion vector. Finally, in each sub-shot undulant and stable sub section. Spatial pyramid matching partitions the key frames into increasingly fine sub-regions and computes features from each sub-region. The proposed method has been implemented and tested on real datasets. The performance has been compared with other standard methods. Experimental results provide an insight of satisfactory results in video retrieval, in terms of both subjective visual perception and objective evaluation metrics like fidelity, video sapling error, precision and recall.
  • Dimensionality Reduction Technique on SIFT Feature Vector for Content Based Image Retrival
    Mukul Kirti Verma, Rajesh Dwivedi, Ajay Kumar Mallick, Ebenezer Jangam
    Communications in Computer and Information Science, 2019
  • Near-duplicate video retrieval based on spatiotemporal Pattern Tree
    Ajay Kumar Mallick, Sushila Maheshkar
    Advances in Intelligent Systems and Computing, 2018
  • Automatic hadoop cluster deployment and management tool
    Sushila Maheshkar, Bhavishya Mathur, Raj Roushan, Ajay Kumar Mallick
    Proceedings 2017 3rd IEEE International Conference on Research in Computational Intelligence and Communication Networks Icrcicn 2017, 2017
  • Video retrieval based on color correlation histogram scheme of clip segmented key frames
    Ajay Kumar Mallick, Sushila Maheshkar
    2016 4th International Conference on Parallel Distributed and Grid Computing Pdgc 2016, 2016
  • Digital image watermarking scheme based on visual cryptography and SVD
    Ajay Kumar Mallick, Priyanka, Sushila Maheshkar
    Advances in Intelligent Systems and Computing, 2016

RECENT SCHOLAR PUBLICATIONS

  • A comprehensive survey of content based image retrieval schemes: advancements, challenges, and future directions
    M Majhi, AK Mallick
    Multimedia Tools and Applications 85 (3), 178 , 2026
    2026
  • Fusion of Deep Cross Block Stage and YOLOv9 for Enhanced Small Object Detection in Aerial Imagery
    S Mirza, AK Mallick
    2025 IEEE International Conference on Computer, Electronics, Electrical … , 2025
    2025
  • AttentiveFP: An Attention-Guided Deep Learning Approach for Fingerprint Liveness Detection
    A Sharma, AK Mallick
    2025 IEEE International Conference on Computer, Electronics, Electrical … , 2025
    2025
  • Fusion of Handcrafted and Deep Convolution Networks Learned Features for Image Retrieval
    S Sharma, AK Mallick
    2025 6th International Conference on Recent Advances in Information … , 2025
    2025
    Citations: 1
  • Revolutionizing Tomato Agriculture: Leaf Disease Detection Using CNN and Its Variants
    M Sood, J Srivastava, AK Mallick
    2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 1-6 , 2024
    2024
    Citations: 1
  • Random projection and hashing based privacy preserving for image retrieval paradigm using invariant and clustered feature
    M Majhi, AK Mallick
    Journal of King Saud University-Computer and Information Sciences 34 (9 … , 2022
    2022
    Citations: 9
  • Video retrieval framework based on color co-occurrence feature of adaptive low rank extracted keyframes and graph pattern matching
    AK Mallick, S Mukhopadhyay
    Information Processing & Management 59 (2), 102870 , 2022
    2022
    Citations: 14
  • Video retrieval using salient foreground region of motion vector based extracted keyframes and spatial pyramid matching
    AK Mallick, S Mukhopadhyay
    Multimedia Tools and Applications 79 (37), 27995-28022 , 2020
    2020
    Citations: 9
  • Video retrieval based on motion vector key frame extraction and spatial pyramid matching
    AK Mallick, S Mukhopadhyay
    2019 6th International Conference on Signal Processing and Integrated … , 2019
    2019
    Citations: 8
  • Dimensionality reduction technique on sift feature vector for content based image retrival
    MK Verma, R Dwivedi, AK Mallick, E Jangam
    International Conference on Recent Trends in Image Processing and Pattern … , 2018
    2018
    Citations: 3
  • Cathedral and indian mughal monument recognition using tensorflow
    A Ninawe, AK Mallick, V Yadav, H Ahmad, DK Sah, C Barna
    International Workshop Soft Computing Applications, 186-196 , 2018
    2018
    Citations: 14
  • Near-duplicate video retrieval based on spatiotemporal pattern tree
    AK Mallick, S Maheshkar
    Proceedings of 2nd International Conference on Computer Vision & Image … , 2018
    2018
    Citations: 2
  • Automatic Hadoop cluster deployment and management tool
    S Maheshkar, B Mathur, R Roushan, AK Mallick
    2017 Third International Conference on Research in Computational … , 2017
    2017
    Citations: 1
  • Video retrieval based on color correlation histogram scheme of clip segmented key frames
    AK Mallick, S Maheshkar
    2016 Fourth International Conference on Parallel, Distributed and Grid … , 2016
    2016
    Citations: 2
  • Digital image watermarking scheme based on visual cryptography and SVD
    AK Mallick, Priyanka, S Maheshkar
    Proceedings of the 4th International Conference on Frontiers in Intelligent … , 2015
    2015
    Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

  • Video retrieval framework based on color co-occurrence feature of adaptive low rank extracted keyframes and graph pattern matching
    AK Mallick, S Mukhopadhyay
    Information Processing & Management 59 (2), 102870 , 2022
    2022
    Citations: 14
  • Cathedral and indian mughal monument recognition using tensorflow
    A Ninawe, AK Mallick, V Yadav, H Ahmad, DK Sah, C Barna
    International Workshop Soft Computing Applications, 186-196 , 2018
    2018
    Citations: 14
  • Random projection and hashing based privacy preserving for image retrieval paradigm using invariant and clustered feature
    M Majhi, AK Mallick
    Journal of King Saud University-Computer and Information Sciences 34 (9 … , 2022
    2022
    Citations: 9
  • Video retrieval using salient foreground region of motion vector based extracted keyframes and spatial pyramid matching
    AK Mallick, S Mukhopadhyay
    Multimedia Tools and Applications 79 (37), 27995-28022 , 2020
    2020
    Citations: 9
  • Video retrieval based on motion vector key frame extraction and spatial pyramid matching
    AK Mallick, S Mukhopadhyay
    2019 6th International Conference on Signal Processing and Integrated … , 2019
    2019
    Citations: 8
  • Digital image watermarking scheme based on visual cryptography and SVD
    AK Mallick, Priyanka, S Maheshkar
    Proceedings of the 4th International Conference on Frontiers in Intelligent … , 2015
    2015
    Citations: 5
  • Dimensionality reduction technique on sift feature vector for content based image retrival
    MK Verma, R Dwivedi, AK Mallick, E Jangam
    International Conference on Recent Trends in Image Processing and Pattern … , 2018
    2018
    Citations: 3
  • Near-duplicate video retrieval based on spatiotemporal pattern tree
    AK Mallick, S Maheshkar
    Proceedings of 2nd International Conference on Computer Vision & Image … , 2018
    2018
    Citations: 2
  • Video retrieval based on color correlation histogram scheme of clip segmented key frames
    AK Mallick, S Maheshkar
    2016 Fourth International Conference on Parallel, Distributed and Grid … , 2016
    2016
    Citations: 2
  • Fusion of Handcrafted and Deep Convolution Networks Learned Features for Image Retrieval
    S Sharma, AK Mallick
    2025 6th International Conference on Recent Advances in Information … , 2025
    2025
    Citations: 1
  • Revolutionizing Tomato Agriculture: Leaf Disease Detection Using CNN and Its Variants
    M Sood, J Srivastava, AK Mallick
    2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 1-6 , 2024
    2024
    Citations: 1
  • Automatic Hadoop cluster deployment and management tool
    S Maheshkar, B Mathur, R Roushan, AK Mallick
    2017 Third International Conference on Research in Computational … , 2017
    2017
    Citations: 1
  • A comprehensive survey of content based image retrieval schemes: advancements, challenges, and future directions
    M Majhi, AK Mallick
    Multimedia Tools and Applications 85 (3), 178 , 2026
    2026
  • Fusion of Deep Cross Block Stage and YOLOv9 for Enhanced Small Object Detection in Aerial Imagery
    S Mirza, AK Mallick
    2025 IEEE International Conference on Computer, Electronics, Electrical … , 2025
    2025
  • AttentiveFP: An Attention-Guided Deep Learning Approach for Fingerprint Liveness Detection
    A Sharma, AK Mallick
    2025 IEEE International Conference on Computer, Electronics, Electrical … , 2025
    2025

STARTUP

Ajay Kumar Mallick completed his B.E in Computer Science and Engineering from the University Institute of Technology, which is affiliated to the University of Burdwan and M.Tech in Computer Science and Engineering from Indian Institute of Technology (Indian School of Mines), Dhanbad, India in 2013 and 2015, respectively. He completed his Ph.D. degree from the Department of Computer Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India. He possesses membership in many technical professional organizations such as IEEE Membership (student) and life membership to the Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI). His research area and interest include content based video retrieval, image processing, and image watermarking, machine learning. He has published articles in many internationally reputed Journals and conferences such as Elsevier, IEEE and Springer Journal as well as conferences.