In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue t...
Graphimages, which represent data structures through nodes and edges, present significant challenges for clustering due to their intricate topological properties. Traditional clustering algorithms, such as K-means and Density-Based Spatial Clustering...
Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision. While traditional clustering methods, such as k...
Efficient clustering of high-spatial-dimensional satellite image datasets remains a critical challenge, particularly due to the computational demands of spectral distance calculations, random centroid initialization, and sensitivity to outliers in co...
Image compression is a critical area of research aimed at optimizing data storage and transmission while maintaining image quality. This paper explores the application of clustering techniques as a means to achieve efficient and high-quality image co...
Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applie...
Multifunctional imaging nanoprobes continue to garner strong interest for their great potential in the detection and monitoring of cancer. In this study, we investigate a series of spatially arranged iron oxide nanocube-based clusters (i.e., chain-li...
Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve...
In image clustering, it is desired that pixels assigned in the same class must be the same or similar. In other words, the homogeneity of a cluster must be high. In gray scale image segmentation, the specified goal is achieved by increasing the numbe...
This paper presents a novel multilook SAR image segmentation algorithm with an unknown number of clusters. Firstly, the marginal probability distribution for a given SAR image is defined by a Gamma mixture model (GaMM), in which the number of compone...
Microarray image processing is a technology for viewing and computationally measuring thousands of genes at the same time. Gene expressions provide information about the cell activity in an organism. Observing a substantial change in gene expressions...
Image annotation is a time-consuming and costly task. Previously, we published MorphoCluster as a novel image annotation tool to address problems of conventional, classifier-based image annotation approaches: their limited efficiency, training set bi...
Visual localization is employed for indoor navigation and embedded in various applications, such as augmented reality and mixed reality. Image retrieval and geometrical measurement are the primary steps in visual localization, and the key to improvin...
Contrast enhancement of images is a crucial topic in image processing that improves the quality of images. The methods of image enhancement are classified into three types, including the histogram method, the fuzzy logic method, and the optimal metho...
An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natu...
Different versions of principal component analysis (PCA) have been widely used to extract important information for image recognition and image clustering problems. However, owing to the presence of outliers, this remains challenging. This paper prop...
It is a challenging problem to efficiently interpret the large volumes of remotely sensed image data being collected in the current age of remote sensing “big data”. Although human visual interpretation can yield accurate annotation of remote sensing...
In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamen...
The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to...
Ensemble clustering combines different basic partitions of a dataset into a more stable and robust one. Thus, cluster ensemble plays a significant role in applications like image segmentation. However, existing ensemble methods have a few demerits, i...
Intensified charge-coupled device (ICCD) images are captured by ICCD sensors in extremely low-light conditions. They often contains spatially clustered noises and general filtering methods do not work well. We find that the scale of the clustered noi...
Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SS...
Microwave staring correlated imaging (MSCI) can achieve ultra-high resolution in real aperture staring radar imaging using the correlated imaging process (CIP) under all-weather and all-day circumstances. The CIP must combine the received echo signal...
Raman microspectroscopy is a powerful, label-free technique for the biochemical characterization of cells, but its complex spectral data require advanced computational methods for meaningful interpretation. Clustering analysis is widely used in spect...
The paper presents a new memetic, cluster-based methodology for image registration in case of geometric perturbation model involving translation, rotation and scaling. The methodology consists of two stages. First, using the sets of the object pixels...
Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to...
Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation applications. Manual labeling-based clustering is both time-consuming and less accurate. Currently, popular methods for extracting features from data use deep l...
Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution hyperspectral (LHS) and high-spatial-resolution multispectral (HMS) image is usually formulated as a spatial super-resolution problem of LHS image with the...
Good learning image priors from the noise-corrupted images or clean natural images are very important in preserving the local edge and texture regions while denoising images. This paper presents a novel image denoising algorithm based on superpixel c...
A vessel automatic identification system (AIS) provides a large amount of dynamic vessel information over a large coverage area and data volume. The AIS data are a typical type of big geo-data with high dimensionality, large noise, heterogeneous dens...
We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and spectral-spatial geometry to dis...
A hierarchic clustering-based enhancement is proposed to solve the luminance compensation of face recognition in the dark field. First, the face image is divided into five levels by a clustering method. Second, the results above are mapped into three...
Hyperspectral image (HSI) clustering is a major challenge due to the redundant spectral information in HSIs. In this paper, we propose a novel deep subspace clustering method that extracts spatial–spectral features via contrastive learning. First, we...
Most existing deep image clustering methods use only class-level representations for clustering. However, the class-level representation alone is not sufficient to describe the differences between images belonging to the same cluster. This may lead t...
Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of w...
Hyperspectral images are becoming a valuable tool much used in agriculture, mineralogy, and so on. The challenge is to successfully classify the materials founded in the field relevant for different applications. Due to a large amount of data corresp...
Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective...
Recently, representation-based subspace clustering algorithms for hyperspectral images (HSIs) have been developed with the assumption that pixels belonging to the same land-cover class lie in the same subspace. Polarization is regarded to be a comple...
This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the she...
This study introduces a palette-based method for the recolorization of cartoon images by combining the k-means clustering algorithm and gradient analysis. The method aims to preserve the visual identity of the original image while allowing flexibilit...
In this study, we propose a fire classification system using image clustering based on a federated learning (FL) structure. This system enables fire detection in various industries, including manufacturing. The accurate classification of fire, smoke,...
Hyperspectral image (HSI) clustering has attracted significant attention due to its broad applications in agricultural monitoring, environmental protection, and other fields. However, the integration of high-dimensional spectral and spatial informati...
Image smoothing is a low-level image processing task mainly aimed at homogenizing an image, mitigating noise, or improving the visibility of certain image areas. There exist two main strategies for image smoothing. The first strategy is content-unawa...
Thermal camera use is becoming ever more widespread in volcanic and environmental research and monitoring activities. Depending on the scope of an investigation and on the type of thermal camera used, different software for thermal infrared (IR) imag...
Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such c...
Unsupervised hyperspectral image (HSI) clustering is a fundamental yet challenging task due to high dimensionality and complex spectral–spatial characteristics. In this paper, we propose a novel and efficient clustering framework centered on ad...
As the volume of satellite images increases rapidly, unsupervised classification can be utilized to swiftly investigate land cover distributions without prior knowledge and to generate training data for supervised (or deep learning-based) classificat...
To address the issue of poor tracking accuracy and the low recognition rate for multiple small targets in infrared images caused by uneven image intensity, this paper proposes an accurate tracking algorithm based on optical flow estimation. The algor...
Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishabl...
Insect diversity monitoring is crucial for biological pest control in agriculture and forestry. Modern monitoring of insect species relies heavily on fine-grained image classification models. Fine-grained image classification faces challenges such as...
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