arXiv subjects

Manzur Murshed

Publications and source records attributed to Manzur Murshed.

At least 19 recordsLinked to original sources

arXiv2026

FAIR: Feature-Augmented Implicit Regularization for AI-generated Fake Image Detection

Generalization remains a critical bottleneck in AI-generated image detection. Because many modern generators are proprietary or adversarially modified, existing detectors overfit to the low-level textural patterns of accessible training data, resulting in severe failures on unseen domains. Conventional regularization techniques (e.g., $L_1$/$L_2$ norms, Dropout) apply indiscriminate parametric constraints and fail to provide the domain-invariant structure necessary for cross-generator robustness. To address this, we propose Feature-Augmented Implicit Regularization (FAIR). FAIR introduces an orthogonal, macro-structural prior, specifically, Scene Composition Structure (SCS), during training to geometrically constrain the model's optimization trajectory. By augmenting the primary feature space with domain-invariant SCS features, FAIR explicitly penalizes texture-biased shortcut learning. Crucially, this structural prior is entirely discarded at inference, yielding a smoothed, generalized decision boundary with zero architectural or computational overhead. Extensive evaluations across five massive benchmarks demonstrate that integrating FAIR into state-of-the-art detectors significantly improves cross-generator generalization, boosting accuracy by up to 8.04% and establishing new state-of-the-art robustness in zero-shot transfer scenarios.

cs.CV↗

arXiv2025

Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification

Invasive species pose major global threats to ecosystems and agriculture. Serrated tussock (\textit{Nassella trichotoma}) is a highly competitive invasive grass species that disrupts native grasslands, reduces pasture productivity, and increases land management costs. In Victoria, Australia, it presents a major challenge due to its aggressive spread and ecological impact. While current ground surveys and subsequent management practices are effective at small scales, they are not feasible for landscape-scale monitoring. Although aerial imagery offers high spatial resolution suitable for detailed classification, its high cost limits scalability. Satellite-based remote sensing provides a more cost-effective and scalable alternative, though often with lower spatial resolution. This study evaluates whether multi-temporal Sentinel-2 imagery, despite its lower spatial resolution, can provide a comparable and cost-effective alternative for landscape-scale monitoring of serrated tussock by leveraging its higher spectral resolution and seasonal phenological information. A total of eleven models have been developed using various combinations of spectral bands, texture features, vegetation indices, and seasonal data. Using a random forest classifier, the best-performing Sentinel-2 model (M76*) has achieved an Overall Accuracy (OA) of 68\% and an Overall Kappa (OK) of 0.55, slightly outperforming the best-performing aerial imaging model's OA of 67\% and OK of 0.52 on the same dataset. These findings highlight the potential of multi-seasonal feature-enhanced satellite-based models for scalable invasive species classification.

cs.CV↗

arXiv2025

A Novel Image Similarity Metric for Scene Composition Structure

The rapid advancement of generative AI models necessitates novel methods for evaluating image quality that extend beyond human perception. A critical concern for these models is the preservation of an image's underlying Scene Composition Structure (SCS), which defines the geometric relationships among objects and the background, their relative positions, sizes, orientations, etc. Maintaining SCS integrity is paramount for ensuring faithful and structurally accurate GenAI outputs. Traditional image similarity metrics often fall short in assessing SCS. Pixel-level approaches are overly sensitive to minor visual noise, while perception-based metrics prioritize human aesthetic appeal, neither adequately capturing structural fidelity. Furthermore, recent neural-network-based metrics introduce training overheads and potential generalization issues. We introduce the SCS Similarity Index Measure (SCSSIM), a novel, analytical, and training-free metric that quantifies SCS preservation by exploiting statistical measures derived from the Cuboidal hierarchical partitioning of images, robustly capturing non-object-based structural relationships. Our experiments demonstrate SCSSIM's high invariance to non-compositional distortions, accurately reflecting unchanged SCS. Conversely, it shows a strong monotonic decrease for compositional distortions, precisely indicating when SCS has been altered. Compared to existing metrics, SCSSIM exhibits superior properties for structural evaluation, making it an invaluable tool for developing and evaluating generative models, ensuring the integrity of scene composition.

cs.CV↗

arXiv2025

ReflectGAN: Modeling Vegetation Effects for Soil Carbon Estimation from Satellite Imagery

Soil organic carbon (SOC) is a critical indicator of soil health, but its accurate estimation from satellite imagery is hindered in vegetated regions due to spectral contamination from plant cover, which obscures soil reflectance and reduces model reliability. This study proposes the Reflectance Transformation Generative Adversarial Network (ReflectGAN), a novel paired GAN-based framework designed to reconstruct accurate bare soil reflectance from vegetated soil satellite observations. By learning the spectral transformation between vegetated and bare soil reflectance, ReflectGAN facilitates more precise SOC estimation under mixed land cover conditions. Using the LUCAS 2018 dataset and corresponding Landsat 8 imagery, we trained multiple learning-based models on both original and ReflectGAN-reconstructed reflectance inputs. Models trained on ReflectGAN outputs consistently outperformed those using existing vegetation correction methods. For example, the best-performing model (RF) achieved an $R^2$ of 0.54, RMSE of 3.95, and RPD of 2.07 when applied to the ReflectGAN-generated signals, representing a 35\% increase in $R^2$, a 43\% reduction in RMSE, and a 43\% improvement in RPD compared to the best existing method (PMM-SU). The performance of the models with ReflectGAN is also better compared to their counterparts when applied to another dataset, i.e., Sentinel-2 imagery. These findings demonstrate the potential of ReflectGAN to improve SOC estimation accuracy in vegetated landscapes, supporting more reliable soil monitoring.

eess.IV↗

arXiv2024

Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data

Recently hyperspectral imaging (HSI)-based grain quality assessment has gained research attention. However, unlike other imaging modalities, HSI data lacks sufficient labelled samples required to effectively train deep convolutional neural network (DCNN)-based classifiers. In this paper, we present a novel approach to grain quality assessment using HSI combined with few-shot learning (FSL) techniques. Traditional methods for grain quality evaluation, while reliable, are invasive, time-consuming, and costly. HSI offers a non-invasive, real-time alternative by capturing both spatial and spectral information. However, a significant challenge in applying DCNNs for HSI-based grain classification is the need for large labelled databases, which are often difficult to obtain. To address this, we explore the use of FSL, which enables models to perform well with limited labelled data, making it a practical solution for real-world applications where rapid deployment is required. We also explored the application of FSL for the classification of hyperspectral images of bulk grains to enable rapid quality assessment at various receival points in the grain supply chain. We evaluated the performance of few-shot classifiers in two scenarios: first, classification of grain types seen during training, and second, generalisation to unseen grain types, a crucial feature for real-world applications. In the first scenario, we introduce a novel approach using pre-computed collective class prototypes (CCPs) to enhance inference efficiency and robustness. In the second scenario, we assess the model's ability to classify novel grain types using limited support examples. Our experimental results show that despite using very limited labelled data for training, our FSL classifiers accuracy is comparable to that of a fully trained classifier trained using a significantly larger labelled database.

cs.CV↗

arXiv2024

FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation

Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional temporal dimension, often exhibit impractical computational complexities with high dimensional data. To address this challenge, we introduce a Deep Learning (DL)-based feature selection algorithm: Feature Selection through Discrete Relaxation (FSDR), tailored for PTS data. Unlike the existing feature selection algorithms, FSDR learns the important features as model parameters using discrete relaxation, which refers to the process of approximating a discrete optimisation problem with a continuous one. FSDR is capable of accommodating a high number of feature dimensions, a capability beyond the reach of existing DL-based or traditional methods. Through testing on a hyperspectral dataset (i.e., a type of PTS data), our experimental results demonstrate that FSDR outperforms three commonly used feature selection algorithms, taking into account a balance among execution time, $R^2$, and $RMSE$.

cs.LG↗

arXiv2022

Depth-based Sampling and Steering Constraints for Memoryless Local Planners

By utilizing only depth information, the paper introduces a novel but efficient local planning approach that enhances not only computational efficiency but also planning performances for memoryless local planners. The sampling is first proposed to be based on the depth data which can identify and eliminate a specific type of in-collision trajectories in the sampled motion primitive library. More specifically, all the obscured primitives' endpoints are found through querying the depth values and excluded from the sampled set, which can significantly reduce the computational workload required in collision checking. On the other hand, we furthermore propose a steering mechanism also based on the depth information to effectively prevent an autonomous vehicle from getting stuck when facing a large convex obstacle, providing a higher level of autonomy for a planning system. Our steering technique is theoretically proved to be complete in scenarios of convex obstacles. To evaluate effectiveness of the proposed DEpth based both Sampling and Steering (DESS) methods, we implemented them in the synthetic environments where a quadrotor was simulated flying through a cluttered region with multiple size-different obstacles. The obtained results demonstrate that the proposed approach can considerably decrease computing time in local planners, where more trajectories can be evaluated while the best path with much lower cost can be found. More importantly, the success rates calculated by the fact that the robot successfully navigated to the destinations in different testing scenarios are always higher than 99.6% on average.

cs.RO↗

arXiv2022

Efficient Motion Modelling with Variable-sized blocks from Hierarchical Cuboidal Partitioning

Motion modelling with block-based architecture has been widely used in video coding where a frame is divided into fixed-sized blocks that are motion compensated independently. This often leads to coding inefficiency as fixed-sized blocks hardly align with the object boundaries. Although hierarchical block-partitioning has been introduced to address this, the increased number of motion vectors limits the benefit. Recently, approximate segmentation of images with cuboidal partitioning has gained popularity. Not only are the variable-sized rectangular segments (cuboids) readily amenable to block-based image/video coding techniques, but they are also capable of aligning well with the object boundaries. This is because cuboidal partitioning is based on a homogeneity constraint, minimising the sum of squared errors (SSE). In this paper, we have investigated the potential of cuboids in motion modelling against the fixed-sized blocks used in scalable video coding. Specifically, we have constructed motion-compensated current frame using the cuboidal partitioning information of the anchor frame in a group-of-picture (GOP). The predicted current frame has then been used as the base layer while encoding the current frame as an enhancement layer using the scalable HEVC encoder. Experimental results confirm 6.71%-10.90% bitrate savings on 4K video sequences.

cs.CV↗

arXiv2021

Human-Machine Collaborative Video Coding Through Cuboidal Partitioning

Video coding algorithms encode and decode an entire video frame while feature coding techniques only preserve and communicate the most critical information needed for a given application. This is because video coding targets human perception, while feature coding aims for machine vision tasks. Recently, attempts are being made to bridge the gap between these two domains. In this work, we propose a video coding framework by leveraging on to the commonality that exists between human vision and machine vision applications using cuboids. This is because cuboids, estimated rectangular regions over a video frame, are computationally efficient, has a compact representation and object centric. Such properties are already shown to add value to traditional video coding systems. Herein cuboidal feature descriptors are extracted from the current frame and then employed for accomplishing a machine vision task in the form of object detection. Experimental results show that a trained classifier yields superior average precision when equipped with cuboidal features oriented representation of the current test frame. Additionally, this representation costs $7\%$ less in bit rate if the captured frames are need be communicated to a receiver.

eess.IV↗

arXiv2021

Integrated Generalized Zero-Shot Learning for Fine-Grained Classification

Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets.

cs.CV↗

arXiv2021

Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning

Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the quality of synthesized features to recognize seen and unseen data. Therefore, learning a joint distribution of seen-unseen domains and preserving domain distinction is crucial for these methods. However, existing methods only learn the underlying distribution of seen data, although unseen class semantics are available in the GZSL problem setting. Most methods neglect retaining domain distinction and use the learned distribution to recognize seen and unseen data. Consequently, they do not perform well. In this work, we utilize the available unseen class semantics alongside seen class semantics and learn joint distribution through a strong visual-semantic coupling. We propose a bidirectional mapping coupled generative adversarial network (BMCoGAN) by extending the coupled generative adversarial network into a dual-domain learning bidirectional mapping model. We further integrate a Wasserstein generative adversarial optimization to supervise the joint distribution learning. We design a loss optimization for retaining domain distinctive information in the synthesized features and reducing bias towards seen classes, which pushes synthesized seen features towards real seen features and pulls synthesized unseen features away from real seen features. We evaluate BMCoGAN on benchmark datasets and demonstrate its superior performance against contemporary methods.

cs.CV↗

arXiv2020

Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation

Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. Prior research has introduced various open set domain adaptation settings in the literature to extend the applications of domain adaptation methods in real-world scenarios. This paper focuses on the type of open set domain adaptation setting where the target domain has both private ('unknown classes') label space and the shared ('known classes') label space. However, the source domain only has the 'known classes' label space. Prevalent distribution-matching domain adaptation methods are inadequate in such a setting that demands adaptation from a smaller source domain to a larger and diverse target domain with more classes. For addressing this specific open set domain adaptation setting, prior research introduces a domain adversarial model that uses a fixed threshold for distinguishing known from unknown target samples and lacks at handling negative transfers. We extend their adversarial model and propose a novel adversarial domain adaptation model with multiple auxiliary classifiers. The proposed multi-classifier structure introduces a weighting module that evaluates distinctive domain characteristics for assigning the target samples with weights which are more representative to whether they are likely to belong to the known and unknown classes to encourage positive transfers during adversarial training and simultaneously reduces the domain gap between the shared classes of the source and target domains. A thorough experimental investigation shows that our proposed method outperforms existing domain adaptation methods on a number of domain adaptation datasets.

cs.CV↗

arXiv2019

Enhanced Transfer Learning with ImageNet Trained Classification Layer

Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained source network are transferred to the target network followed by fine-tuning. Prior research has shown that this approach is capable of improving task performance. However, the impact of the ImageNet pre-trained classification layer in parameter fine-tuning is mostly unexplored in the literature. In this paper, we propose a fine-tuning approach with the pre-trained classification layer. We employ layer-wise fine-tuning to determine which layers should be frozen for optimal performance. Our empirical analysis demonstrates that the proposed fine-tuning performs better than traditional fine-tuning. This finding indicates that the pre-trained classification layer holds less category-specific or more global information than believed earlier. Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task. Our study manifests that careful normalization and scaling are essential for creating harmony between the pre-trained and new layers for target domain adaptation. We evaluate the proposed depth augmented networks for fine-tuning on several challenging benchmark datasets and show that they can achieve higher classification accuracy than contemporary transfer learning approaches.

cs.CV↗

arXiv2019

Transfer Learning Using Classification Layer Features of CNN

Although CNNs have gained the ability to transfer learned knowledge from source task to target task by virtue of large annotated datasets but consume huge processing time to fine-tune without GPU. In this paper, we propose a new computationally efficient transfer learning approach using classification layer features of pre-trained CNNs by appending layer after existing classification layer. We demonstrate that fine-tuning of the appended layer with existing classification layer for new task converges much faster than baseline and in average outperforms baseline classification accuracy. Furthermore, we execute thorough experiments to examine the influence of quantity, similarity, and dissimilarity of training sets in our classification outcomes to demonstrate transferability of classification layer features.

cs.CV↗

arXiv2018

Depth Sequence Coding with Hierarchical Partitioning and Spatial-domain Quantisation

Depth coding in 3D-HEVC for the multiview video plus depth (MVD) architecture (i) deforms object shapes due to block-level edge-approximation; (ii) misses an opportunity for high compressibility at near-lossless quality by failing to exploit strong homogeneity (clustering tendency) in depth syntax, motion vector components, and residuals at frame-level; and (iii) restricts interactivity and limits responsiveness of independent use of depth information for "non-viewing" applications due to texture-depth coding dependency. This paper presents a standalone depth sequence coder, which operates in the lossless to near-lossless quality range while compressing depth data superior to lossy 3D-HEVC. It preserves edges implicitly by limiting quantisation to the spatial-domain and exploits clustering tendency efficiently at frame-level with a novel binary tree based decomposition (BTBD) technique. For mono-view coding of standard MVD test sequences, on average, (i) lossless BTBD achieved $\times 42.2$ compression-ratio and $-60.0\%$ coding gain against the pseudo-lossless 3D-HEVC, using the lowest quantisation parameter $QP = 1$, and (ii) near-lossless BTBD achieved $-79.4\%$ and $6.98$ dB Bjøntegaard delta bitrate (BD-BR) and distortion (BD-PSNR), respectively, against 3D-HEVC. In view-synthesis applications, decoded depth maps from BTBD rendered superior quality synthetic-views, compared to 3D-HEVC, with $-18.9\%$ depth BD-BR and $0.43$ dB synthetic-texture BD-PSNR on average.

cs.IT↗

arXiv2017

Multi-objective, Decentralized Dynamic Virtual Machine Consolidation using ACO Metaheuristic in Computing Clouds

Underutilization of computing resources and high power consumption are two primary challenges in the domain of Cloud resource management. This paper deals with these challenges through offline, migration impact-aware, multi-objective dynamic Virtual Machine (VM) consolidation in the context of large-scale virtualized data center environments. The problem is formulated as an NP-hard discrete combinatorial optimization problem with simultaneous objectives of minimizing resource wastage, power consumption, and the associated VM migration overhead. Since dynamic VM consolidation through VM live migrations have negative impacts on hosted applications performance and data center components, a VM live migration overhead estimation technique is proposed, which takes into account pragmatic migration parameters and overhead factors. In order to tackle scalability issues, a hierarchical, decentralized dynamic VM consolidation framework is presented that helps to localize migration-related network traffic and reduce network cost. Moreover, a multi-objective, dynamic VM consolidation algorithm is proposed by utilizing the Ant Colony Optimization (ACO) metaheuristic, with integration of the proposed VM migration overhead estimation technique. Comprehensive performance evaluation makes it evident that the proposed dynamic VM consolidation approach outpaces the state-of-the-art offline, migration-aware dynamic VM consolidation algorithm across all performance metrics by reducing the overall power consumption by up to 47%, resource wastage by up to 64%, and migration overhead by up to 83%.

cs.DC↗

arXiv2017

An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.

cs.DC↗

arXiv2014

Modified Rice-Golomb Code for Predictive Coding of Integers with Real-valued Predictions

Rice-Golomb codes are widely used in practice to encode integer-valued prediction residuals. However, in lossless coding of audio, image, and video, specially those involving linear predictors, the predictions are from the real domain. In this paper, we have modified and extended the Rice-Golomb code so that it can operate at fractional precision to efficiently exploit the real-valued predictions. Coding at arbitrarily small precision allows the residuals to be modeled with the Laplace distribution instead of its discrete counterpart, namely the two-sided geometric distribution (TSGD). Unlike the Rice-Golomb code, which maps equally probable opposite-signed residuals to different integers, the proposed coding scheme is symmetric in the sense that, at arbitrarily small precision, it assigns codewords of equal length to equally probable residual intervals. The symmetry of both the Laplace distribution and the code facilitates the analysis of the proposed coding scheme to determine the average code-length and the optimal value of the associated coding parameter. Experimental results demonstrate that the proposed scheme, by making efficient use of real-valued predictions, achieves better compression as compared to the conventional scheme.

cs.IT↗