Browsing by Author "Mona Singh"

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A Comparative Study of Feature Extraction Techniques and Similarity Measures for Image Retrieval
(Institute of Electrical and Electronics Engineers Inc., 2022) Mona Singh; Suneel Kumar; Ruchilekha; Manoj Kumar Singh
With the growing popularity of using massive amount image database in several applications, it is critical to develop an autonomous and efficient retrieval system to search the relevant images from entire database. The method of obtaining the relevant images from huge image libraries by extracting their content features is known as content-based image retrieval (CBIR). In this paper, a comparative study is performed while acquiring various methods of traditional feature extraction, such as Color moment, Gabor wavelet, Discrete wavelet transform (DWT), Local binary pattern (LBP), Gray level co-occurrence matrix (GLCM), and Histogram of orientation (HOG), to present an efficient and more accurate CBIR system. The experiment is demonstrated on two benchmark datasets, namely Wang (color images) and Medical MNIST (grayscale images), with different visual effects. To retrieve relevant images of a query image, three distinct distance metrics, such as Cosine, City block, and Euclidean, are used to examine the similarity between the query image and the database images. The experiment is evaluated using two performance metrics: precision and recall, to compare the efficacy of various approach. We achieve the best results as average precision of 65.65% and average recall of 6.57 on a scale of 10 using Color moment features via Euclidean distance metric in case of WANG dataset, while 99.89% and 9.99 on a scale of 10 for average precision and average recall using HOG features via City block distance metric in case of Medical MNIST dataset. © 2022 IEEE.
A deep learning approach for subject-dependent & subject-independent emotion recognition using brain signals with dimensional emotion model
(Elsevier Ltd, 2023) Ruchilekha; Manoj Kumar Singh; Mona Singh
This paper aims to design a deep-learning based approach in combination with machine learning classifiers for two different perspectives. In first perspective, the performance is evaluated when training and testing are performed on same subject called as subject–dependent evaluation criteria. In second perspective, the performance is evaluated when training and testing are performed on different subjects called as subject–independent evaluation criteria. For each perspective, three label cases are made using valence, arousal, and dominance for recognizing human emotions: i) Binary/ 2-class, ii) Quad/ 4-class, and iii) Octal/ 8-class classifications. The experiment is performed on two publicly available datasets DEAP and DREAMER. For emotion recognition, firstly the brain signals are processed and then features are extracted using our proposed deep convolutional neural network (DCNN) architecture. These extracted features are used for emotion recognition using classifiers namely Naive Bayes (NB), decision tree (DT), k-Nearest Neighborhood (KNN), Support Vector Machine (SVM), AdaBoost (AB), Random Forest (RF), Neural Networks (NN), Long-short term memory (LSTM), and Bidirectional-LSTM (BiLSTM). The experimental results give more robust classification for subject-independent emotion recognition in comparison to subject-dependent emotion recognition, with DCNN + NN for binary and DCNN + SVM for quad & octal classification. Moreover, experimental results show that arousal and dominance play an important role in emotion recognition in contrary to valence and arousal as reported in literature. © 2023 Elsevier Ltd
An Effective Deep Learning Model for Content-Based Gastric Image Retrieval
(Institute of Electrical and Electronics Engineers Inc., 2023) Mona Singh; Manoj Kumar Singh
In this paper, we propose a feature combination, also known as feature fusion, for improving performance in content-based gastric image retrieval (CBGIR). This study provides a CBGIR system that retrieves images by combining ResNet-18 and ResNet-50 information and finally, the Euclidean distance metric is evaluated for similarity measurement. The proposed approach is also compared to different deep learning techniques such as AlexNet, VGGs (VGG-16 & VGG-19), GoogleNet, SqueezeNet, DarkNet-19 models. The proposed method was examined on the KVASIR database with 4000 images and S different classes. We get the optimum results as average precision of 95.44% and average recall of 19.09 on a scale of 20 using the proposed deep learning model and Euclidean distance metric. . © 2023 IEEE.
Content based medical image retrieval using deep learning and handcrafted features in dimensionality reduction framework
(Elsevier Ltd, 2025) Mona Singh; Manoj Kumar Singh
Content-based medical image retrieval (CBMIR) is an approach utilized for extracting pertinent medical images from extensive databases by focusing on their visual attributes instead of relying on textual information. This method entails examining the visual qualities of medical images, including texture, shape, intensity, and spatial relationships, in order to detect resemblances and patterns. This study addresses two major challenges in CBMIR: effective image representation and dimensionality reduction. The semantic gap between human interpretation and machine-generated features is tackled using handcrafted techniques and deep convolutional neural networks (DCNNs) using transfer learning for extracting features. Additionally, dimensionality reduction methods: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Uniform Manifold Approximation and Projection (UMAP), and t-distributed Stochastic Neighbor Embedding (t-SNE), are evaluated to optimize performance in terms of accuracy, speed, scalability, and memory usage. The performance of the CBMIR system is evaluated using four datasets: Medical MNIST, KVASIR, PH2, and MESSIDOR and evaluated using metrics: Precision, Recall, and F1-score. Results show that proposed method, HOG + t-SNE, maintains constant performance with mean average precision (mAP) 99.85% compared to the full-dimension feature based technique on Medical MNIST, and the performance of the DCNNs with various dimensionality reduction methods is evaluated on KVASIR, MESSIDOR and PH2 datasets and found that our proposed method, GoogleNet + t-SNE, achieves mAP of 95.32%, 92.33%, and 91.34% respectively. © 2025
Content-Based Gastric Image Retrieval Using Fusion of Deep Learning Features with Dimensionality Reduction
(Springer, 2025) Mona Singh; Manoj Kumar Singh
The rapid expansion of medical imaging repositories in hospitals has introduced significant challenges in managing and retrieving relevant data, which may contribute to diagnostic errors. Content-based medical image retrieval (CBMIR) offers a solution to these challenges by enabling efficient querying of vast datasets. This research introduces an efficient method, ResNetFuse, which leverages pre-trained deep convolutional neural networks (DCNNs), ResNet-18 and ResNet-50, for feature extraction. In ResNetFuse, the features from both networks are fused via concatenation, resulting in substantial improvements in retrieval performance. However, this fusion increases the dimensionality of the features, that leads to increase in the storage and time for retrieval process. To address the high dimensionality issue, here we used t-distributed stochastic neighbour embedding (t-SNE). The proposed ResNetFuse + t-SNE method is rigorously evaluated on the KVASIR benchmark dataset. Experimental results demonstrate that ResNetFuse + t-SNE surpasses state-of-the-art techniques across performance metrics, achieving a mean average precision (mAP) of 96.15% for the retrieval of 10 images. Additionally, the method achieves an 87.5% reduction in feature dimensionality compared to ResNetFuse alone, facilitating more compact and efficient image indexing without sacrificing retrieval accuracy. These findings underscore the efficacy of ResNetFuse + t-SNE in improving retrieval performance while reducing computational complexity, making it particularly suitable for resource-constrained environments. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Semantic Segmentation Based Image Signature Generation for CBIR
(Springer Science and Business Media Deutschland GmbH, 2024) Suneel Kumar; Mona Singh; Ruchilekha; Manoj Kumar Singh
Content-Based Image Retrieval (CBIR) leveraging semantic segmentation integrates semantic understanding with image retrieval, enabling users to search for images based on specific objects or regions within them. This paper presents a methodology for constructing image signatures, a pivotal element in enhancing image representation within a CBIR system. The efficiency and effectiveness of a CBIR system significantly hinge on the quality of the image signature, which serves as a compact and informative representation of raw image data. Our proposed methodology begins with emphasizing clear object or region boundaries through pixel-level semantic segmentation masks. A pretrained semantic segmentation model, such as DeepLab v3+, is employed to generate pixel-wise object class predictions, yielding the necessary segmentation masks. Subsequently, each image is segmented into meaningful regions based on these masks, and relevant features are extracted from each segmented region using a pre-trained Deep Convolutional Neural Network (DCNN) models AlexNet, VGG16 and ResNet-18. During the retrieval phase, when a user queries the system with an image, the query image is segmented using the pre-trained semantic segmentation model, and features are extracted from the segmented regions of the query image. These query features are utilized to search the database for the most similar regions or images. Similarity scores, calculated using Euclidean distance, are used to rank the database entries based on their similarity to the query, allowing for efficient retrieval of the top-k most similar regions or images. We found that for some classes semantic segmented based retrieval better performance in comparison to image based. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.