Simple Linear Iterative Clustering Algorithm Research Articles

Adaptive superpixel segmentation and pigment identification of colored relics based on visible spectral images

This work explores the extraction of the spatial distribution and chemical composition information of pigments in colored relics through visible spectral images. An adaptive superpixel segmentation method is proposed to extract the spatial distribution information of pigments. Quadtree decomposition is applied to generate nonuniform initial seed points based on the image homogeneity. These seed points are used as the initial cluster centers in an extended simple linear iterative clustering (SLIC) algorithm for visible spectral images to create superpixels of varying sizes that reflect the homogeneity. Each superpixel is subsequently treated as an individual area in the colored relics, and a pigment identification method based on the visible spectral reflectance is proposed to identify the pigments in these areas. A standard reference database is constructed using samples that simulate the painting process of ancient wall paintings in the Mogao Grottoes. Geometric features, which are characterized by the linear combination of the normalized visible spectral reflectance, its slope and its curvature, are designed to represent the chemical composition of pigments. The geometric features of the superpixels are compared with those of the pigments in the database using the Euclidean distance to determine the pigments in each area of the colored relics. This work is expected to provide scientific guidance for pigment selection in the color restoration of colored relics.

Infrared superpixel patch-image model for small target detection under complex background

The main problem of infrared small target detection in complex background is how to effectively eliminate the edge residue. In this paper, we propose an efficient method named Superpixel Patch Image (SPI) model to handle this challenging task. The SPI model can fit the edges of the background well, thus effectively eliminating edge interference in the process of target detection, and achieving excellent performance. The SPI method consists of three steps: Firstly, an improved Simple Linear Iterative Clustering (ISLIC) algorithm is proposed to generate compact superpixels that perfectly match the background edge. Secondly, setting each superpixel patch as a column, a large patch-image matrix is constructed, and the target foreground image and background image is separated by imprecisely augmented Lagrange multiplication. Finally, based on the comprehensively analysis of the distribution characteristics of the target and the highlighted edge in the foreground image, an adaptive threshold is used to extract the target from the foreground superpixel patch. The experimental results of real infrared scenes show that the presented SPI model achieves the best SCRG, BSF and ROC curves compared with the existing 9 state-of-art algorithms, and can effectively extract small targets under different complex backgrounds.

SPMUNet: Semantic segmentation of citrus surface defects driven by superpixel feature

The segmentation of surface defects in citrus fruit makes a significant contribution to quality assessment and post-harvest processing. However, semantic segmentation of citrus surface defects remains challenging owing to the complexity of defect features, unclear defect boundaries and semantic gaps in models. Therefore, a superpixel driven and multiscale channel cross-fusion transformer (MCCT)-based UNet (named SPMUNet) was proposed to segment citrus surface defects. The model first created a multiscale superpixel region feature (MSRF) branch using the simple linear iterative cluster (SLIC) algorithm to form the key features embedded in the downsampling layer. Then, the proposed Superpixel Loss optimized the loss function based on superpixel grid to provide error information for boundary segmentation. In addition, the MCCT module was used to improve the skip connection layers, and feature fusion was achieved by collaborative learning. Finally, the performance of the model was validated through ablation studies, feature map visualization, and comparison experiments. The results showed that the mPA, mIoU, and dice coefficients of SPMUNet for citrus surface segmentation were 89.1%, 83.8%, and 88.6%, respectively. Compared with the baseline, the proposed MSRF branch, MCCT, and Superpixel Loss improved mIoU by 2.6%, 1.3%, and 4.2%, respectively. And the effectiveness of the proposed methods was also validated by the feature map visualization. Furthermore, the proposed SPMUNet achieved optimal segmentation performance compared to other semantic segmentation models (UNet, ResUNet++, DeepLabv3+, SwinUNet, TransUNet). SPMUNet also excelled in the segmentation of small targets and boundaries, improving by 10.2% and 7.0%, respectively, over the baseline. The improvement in segmentation performance demonstrated the effectiveness of the selected superpixel features. The proposed citrus surface defect segmentation model achieved high-precision segmentation and serves as a technical reference for citrus sorting and quality assessment.

Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network

Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network

A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

Copy-Move Forgery (CMF) is a common form of image manipulation attack that involves copying and pasting a part of an image to another position within the same image. This study proposes a Deep Learning (DL) model for detecting CMF, particularly in the presence of various malicious attacks. The proposed approach involves several steps, including converting the input image to grayscale, preprocessing the image using the Simple Linear Iterative Clustering (SLIC) algorithm to generate superpixel partitions, and then extracting keypoint features using the Speeded Up Robust Features (SURF) detector. Finally, a Generative Adversarial Network (GAN) is employed for feature description and matching. To assess the effectiveness of the approach, the types of features used for copy-move forgery were addressed. The proposed approach was examined under rotation, blurring, jpg compression, and scaling attacks. Furthermore, experimental results showed that the proposed approach can detect multiple CMFs with high accuracy. Finally, the proposed method was compared with recent state-of-the-art methods.

Optic disc detection based on fully convolutional network and weighted matrix recovery model.

Eye diseases often affect human health. Accurate detection of the optic disc contour is one of the important steps in diagnosing and treating eye diseases. However, the structure of fundus images is complex, and the optic disc region is often disturbed by blood vessels. Considering that the optic disc is usually a saliency region in fundus images, we propose a weakly-supervised optic disc detection method based on the fully convolution neural network (FCN) combined with the weighted low-rank matrix recovery model (WLRR). Firstly, we extract the low-level features of the fundus image and cluster the pixels using the Simple Linear Iterative Clustering (SLIC) algorithm to generate the feature matrix. Secondly, the top-down semantic prior information provided by FCN and bottom-up background prior information of the optic disc region are used to jointly construct the prior information weighting matrix, which more accurately guides the decomposition of the feature matrix into a sparse matrix representing the optic disc and a low-rank matrix representing the background. Experimental results on the DRISHTI-GS dataset and IDRiD dataset show that our method can segment the optic disc region accurately, and its performance is better than existing weakly-supervised optic disc segmentation methods. Graphical abstract of optic disc segmentation.

Edge Detection Method for High-Resolution Remote Sensing Imagery by Combining Superpixels with Dual-Threshold Edge Tracking

Edge detection in high-spatial-resolution remote sensing images (HSRIs ) is a key technology for automatic extraction, analysis, and understanding of image information. With respect to the problem of fake edges in image edge detection caused by image noise and the phenomenon of the same class objects reflecting different spectra, this article proposes a novel edge detection method for HSRIs by combin- ing superpixels with dual-threshold edge tracking. First, the image is smoothed using the simple linear iterative clustering algorithm to eliminate the influence of image noise and the phenomenon of the same class objects reflecting different spectra on image edge detec - tion. Second, initial edge detection results of the image are obtained using the dual-threshold edge tracking algorithm. Finally, the initial image edge detection results are post-processed by removing the burrs and extracting skeleton lines to obtain accurate edge detection results. The experimental results confirm that the proposed method outperforms the others and can obtain smooth, continuous, and single-pixel response edge detection results for HSRIs .

SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network

To address key issues with traditional landform classification methods that impact the integrity and continuity of the analysis area, as well as neglecting neighboring features, We presents a novel approach called superpixel-based graph convolutional network (SGCN) for automatic classification of digital elevation model (DEM) landform. The SGCN uses superpixel segmentation and graph convolutional networks (GCNs). Specifically, we employ the Simple Linear Iterative Clustering (SLIC) algorithm to perform superpixel segmentation on the DEM image and convert the resulting distribution of superpixels in the 2D image into a graph structure, where nodes are labeled. We then analyze the geographic features and external neighborhood relationships within each DEM superpixel to obtain corresponding node information for the graph structure. Finally, the GCN model is trained according to the graph structure, node feature information, and node label, enabling automatic recognition of node landform types. Importantly, SGCN accounts for the multi-order neighborhood features of the basic analysis unit and expresses the DEM micro-features relatively macroscopically. Experimental results demonstrate that compared with pixel-level semantic segmentation methods like U-Net, FCN, and SegNet, the SGCN significantly improves DEM landform classification accuracy by 17.84%, 33.22%, and 30.60%, respectively. Moreover, SGCN better preserves the integrity and continuity of features inside and outside the analysis area, overcoming the limitation of traditional methods that use regular grids as the basic analysis unit, which can compromise landform edge continuity.

A no-reference panoramic image quality assessment with hierarchical perception and color features

The ultimate goal of no-reference omnidirectional image quality assessment (NR-OIQA) is to design a comprehensive perception method that can accurately assess the quality of damaged omnidirectional images without prior knowledge. However, most existing studies cannot attain credible accuracy because of lacking a non-neuroscience-based or non-biology-based model. Inspired by this, an original visual perception-based and neuroscience-based OIQA model by considering the hierarchical perception features of the HVS, which includes specific information, local saliency information, global information, and color information which is often ignored by researchers is proposed in this work. According to the hierarchical process in neuroscience, the high-frequency co-occurrence matrix (HFCM)-based and variance-based specific features are applied to perceive details that are first distorted in the frequency domain. The entropy-based combination of the paranormal saliency map(PSM) and superpixel segmentation with the simple linear iterative clustering (SLIC) algorithm is employed to emphasize the rich quality-aware local saliency information. The global panoramic statistical (GPS) model is utilized to express global semantics distortion as the high-level feature. Visual-aware color texture descriptor with the cross-channel local binary pattern(CCLBP) which effectively reflects the correlation and dependency of pixels between different color channels is employed to map color information. Finally, all above features have been extracted and combined with subjective scores to assess the objective quality scores by support vector regression (SVR). Experiments express that our method has more accuracy and stronger stability on CVIQD2018 and OIQA databases.

Super pixels transmission map-based object detection using deep neural network in UAV video

ABSTRACT Object detection has become a very prominent subject for research in recent times. This study's main goal is to suggest a technique for video saliency object detection. It seems to sense that using the depth information in photos to detect salient things. Since depth offers abundant information about scene structure, object forms, and other 3D cues. This information is very compatible to distinguish between objects in the foreground and background. As a result of the high object density, small object size, and cluttered background, aerial photos and movies provide results with low precision. In this paper, the proposed SPTM (Super Pixel Transmission Map)-YOLO model, the input RGB image has applied Dark Channel Prior (DCP) method for estimating the transmission map. From the transmission map only, the background probability is estimated with the help of SLIC (simple linear iterative clustering algorithm) superpixel segmentation. That foreground extracted image is further learned with YOLO architecture to detect the objects effectively. For object detection in aerial images, this proposed SPTM-YOLO approach outperforms classic YOLO by up to 6% accuracy. Accurate detection of things that are small in size, partially occluded, and out of view is possible.

Complex Traffic Scene Image Classification Based on Sparse Optimization Boundary Semantics Deep Learning

With the rapid development of intelligent traffic information monitoring technology, accurate identification of vehicles, pedestrians and other objects on the road has become particularly important. Therefore, in order to improve the recognition and classification accuracy of image objects in complex traffic scenes, this paper proposes a segmentation method of semantic redefine segmentation using image boundary region. First, we use the SegNet semantic segmentation model to obtain the rough classification features of the vehicle road object, then use the simple linear iterative clustering (SLIC) algorithm to obtain the over segmented area of the image, which can determine the classification of each pixel in each super pixel area, and then optimize the target segmentation of the boundary and small areas in the vehicle road image. Finally, the edge recovery ability of condition random field (CRF) is used to refine the image boundary. The experimental results show that compared with FCN-8s and SegNet, the pixel accuracy of the proposed algorithm in this paper improves by 2.33% and 0.57%, respectively. And compared with Unet, the algorithm in this paper performs better when dealing with multi-target segmentation.

Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF

The semantic segmentation of remote sensing images is a significant research direction in digital image processing. The complex background environment, irregular size and shape of objects, and similar appearance of different categories of remote sensing images have brought great challenges to remote sensing image segmentation tasks. Traditional convolutional-neural-network-based models often ignore spatial information in the feature extraction stage and pay less attention to global context information. However, spatial context information is important in complex remote sensing images, which means that the segmentation effect of traditional models needs to be improved. In addition, neural networks with a superior segmentation performance often suffer from the problem of high computational resource consumption. To address the above issues, this paper proposes a combination model of a modified multiscale deformable convolutional neural network (mmsDCNN) and dense conditional random field (DenseCRF). Firstly, we designed a lightweight multiscale deformable convolutional network (mmsDCNN) with a large receptive field to generate a preliminary prediction probability map at each pixel. The output of the mmsDCNN model is a coarse segmentation result map, which has the same size as the input image. In addition, the preliminary segmentation result map contains rich multiscale features. Then, the multi-level DenseCRF model based on the superpixel level and the pixel level is proposed, which can make full use of the context information of the image at different levels and further optimize the rough segmentation result of mmsDCNN. To be specific, we converted the pixel-level preliminary probability map into a superpixel-level predicted probability map according to the simple linear iterative clustering (SILC) algorithm and defined the potential function of the DenseCRF model based on this. Furthermore, we added the pixel-level potential function constraint term to the superpixel-based Gaussian potential function to obtain a combined Gaussian potential function, which enabled our model to consider the features of various scales and prevent poor superpixel segmentation results from affecting the final result. To restore the contour of the object more clearly, we utilized the Sketch token edge detection algorithm to extract the edge contour features of the image and fused them into the potential function of the DenseCRF model. Finally, extensive experiments on the Potsdam and Vaihingen datasets demonstrated that the proposed model exhibited significant advantages compared to the current state-of-the-art models.

Minimum spanning tree dynamic programming stereo‐matching method based on superpixels

AbstractThe minimum spanning tree (MST) stereo‐matching method is an information‐infiltration process. The difference in edge attributes of an MST can cause the edge‐expansion phenomenon, which affects the matching accuracy. To accurately recover image‐depth information, a dynamic‐programming stereo‐matching method based on the MST was proposed. First, the colour Birchfield Tomasi cost‐calculation method based on image adaptive colour information was proposed to obtain stable initial cost values. Second, the image was segmented into superpixel regions using the simple linear iterative clustering algorithm. The pixel‐ and region‐level MSTs were then constructed. Next, combined with the idea of dynamic programming, the MST cost‐aggregation process was re‐deduced. On this basis, the aggregate cost values of the two MSTs were obtained. Finally, the aggregate cost values were combined adaptively to acquire the high‐precision smooth disparity map. The Middlebury 2014 dataset was used for the experiments. The experimental results indicate that the proposed method can effectively improve the accuracy of stereo‐matching.

Automated Space Charge Classification Inside ±500-kV HVDC Cable Insulation Using Fusion of Superpixel and Deep Features for Remote Condition Assessment

The accumulated space charges cause electrical field distortion, which is fatal to the safe and reliable operation of polymeric high-voltage direct current (HVDC) cables. Hence, this paper aims to detect and classify the space charges to ensure reliability and a longer operating life of HVDC cables. To achieve this, experiments were carried out on cross-linked polyethylene (XLPE) insulation samples and space charge distributions were recorded under altering electric fields (10–50 kV/mm) and at different temperatures (30–70 °C). Subsequently, super-pixel color features were extracted from the space charge images using the simple linear iterative clustering (SLIC) algorithm. In addition, deep features were extracted using the AlexNet convolutional neural network (CNN) model. The fusion of the handcrafted and deep features was fed to three benchmark machine-learning classifiers for the recognition of different space charge accumulation categories. The method delivered high recognition performance in spite of altering electric fields and varying temperatures. As a result, the proposed framework can detect space charges in HVDC cable insulation in real time.

Superpixel-Based Multiscale CNN Approach Toward Multiclass Object Segmentation From UAV-Captured Aerial Images

Unmanned aerial vehicles (UAVs) are promising remote sensors capable of reforming remote sensing applications. However, for artificial intelligence (AI)-guided tasks, like land cover mapping and ground-object mapping, most deep learning-based architectures fail to extract scale-invariant features, resulting in poor performance accuracy. In this context, the article proposes a superpixel-aided multiscale convolutional neural network (CNN) architecture to avoid misclassification in complex urban aerial images.The proposed framework is a two-tier deep learning-based segmentation architecture. In the first stage, a superpixel-based simple linear iterative cluster (SLIC) algorithm produces superpixel images with crucial contextual information. The second stage comprises a multiscale CNN architecture that uses these information-rich superpixel images to extract scale-invariant features for predicting the object class of each pixel. Two UAV-image-based aerial image datasets: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NITRDrone</i> dataset and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">urban drone dataset</i> (UDD) are considered to perform the experimentation. The proposed model outperforms the considered state-of-the-art methods with an intersection of union (IoU) of 76.39% and 86.85% on UDD and NITRDrone datasets, respectively. Experimentally obtained results prove that the proposed architecture performs superior by achieving better performance accuracy in complex and challenging scenarios.

Recognition of necrotic regions in MRI images of chronic spinal cord injury based on superpixel

Background and objectiveThe cystic cavity and its surrounding dense glial scar formed in chronic spinal cord injury (SCI) hinder the regeneration of nerve axons. Accurate location of the necrotic regions formed by the scar and the cavity is conducive to eliminate the re-growth obstacles and promote SCI treatment. This work aims to realize the accurate and automatic location of necrotic regions in the chronic SCI magnetic resonance imaging (MRI). MethodsIn this study, a method based on superpixel is proposed to identify the necrotic regions of spinal cord in chronic SCI MRI. Superpixels were obtained by a simple linear iterative clustering algorithm, and feature sets were constructed from intensity statistical features, gray level co-occurrence matrix features, Gabor texture features, local binary pattern features and superpixel areas. Subsequently, the recognition effects of support vector machine (SVM) and random forest (RF) classification model on necrotic regions were compared from accuracy (ACC), positive predictive value (PPV), sensitivity (SE), specificity (SP), Dice coefficient and algorithm running time. ResultsThe method is evaluated on T1- and T2-weighted MRI spinal cord images of 24 adult female Wistar rats. And an automatic recognition method for spinal cord necrosis regions was established based on the SVM classification model finally. The recognition results were 1.00±0.00 (ACC), 0.89±0.09 (PPV), 0.88±0.12 (SE), 1.00±0.00 (SP) and 0.88±0.07 (Dice), respectively. ConclusionsThe proposed method can accurately and noninvasively identify the necrotic regions in MRI, which is helpful for the pre-intervention assessment and post-intervention evaluation of chronic SCI research and treatments, and promoting the clinical transformation of chronic SCI research.

Estimation of sub-endmembers using spatial-spectral approach for hyperspectral images

In Blind Hyperspectral Unmixing, the accuracy of the estimated number of endmembers affects the succeeding steps of extraction of endmember signatures and acquiring their fractional abundances. The characteristics of endmember signature depend on the nature of the material on the ground and share similar characteristics for variants of the same material. In this paper, we introduce a new concept of sub-endmembers to identify similar materials that are variants of a global endmember. Identifying the sub-endmembers will provide a meaningful interpretation of the endmember variability along with increased unmixing accuracy. This paper proposes a new algorithm exploiting both the spatial and spectral information present in hyperspectral data. The hyperspectral data are segmented into homogenous regions (superpixels) based on the Simple Linear Iterative Clustering (SLIC) algorithm, and the mean spectral of each region is accounted for in finding the global endmembers. The difference of eigenvalues-based thresholding method is used to find the number of global and sub-endmembers. The method has been tested on synthetic and real hyperspectral data and has successfully estimated the number of global endmembers as well as sub-endmembers. The method is also compared with other state-of-the-art methods, and better performances are obtained at a reasonably lower computational complexity.

Optimized method for segmentation of ancient mural images based on superpixel algorithm.

High-precision segmentation of ancient mural images is the foundation of their digital virtual restoration. However, the complexity of the color appearance of ancient murals makes it difficult to achieve high-precision segmentation when using traditional algorithms directly. To address the current challenges in ancient mural image segmentation, an optimized method based on a superpixel algorithm is proposed in this study. First, the simple linear iterative clustering (SLIC) algorithm is applied to the input mural images to obtain superpixels. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to cluster the superpixels to obtain the initial clustered images. Subsequently, a series of optimized strategies, including (1) merging the small noise superpixels, (2) segmenting and merging the large noise superpixels, (3) merging initial clusters based on color similarity and positional adjacency to obtain the merged regions, and (4) segmenting and merging the color-mixing noisy superpixels in each of the merged regions, are applied to the initial cluster images sequentially. Finally, the optimized segmentation results are obtained. The proposed method is tested and compared with existing methods based on simulated and real mural images. The results show that the proposed method is effective and outperforms the existing methods.

Satellite IoT Based Road Extraction from VHR Images Through Superpixel-CNN Architecture

In the past few decades, technology has progressively become ineluctable in human lives, primarily due to the growth of certain fields like space technology, Big Data, the Internet of Things (IoT), and machine learning. Space technology has revolutionized communication mechanisms while creating opportunities for various research areas, including remote sensing (RS)-inspired applications. On the other hand, IoT presents a platform to use the power of the internet over a whole range of devices through a phenomenon known as social IoT. These devices generate a humongous amount of data that requires handling and managing by big data technology incorporated with deep learning techniques to reduce the manual workload of an operator. Moreover, deep learning architectures like convolutional neural networks (CNNs) have presented a scope to extract the underlying features from the large-scale input images in providing better solutions for tasks such as automatic road detection that come at the cost of time and memory overhead. In this context, we have proposed a three-layer edge-fog-cloud-based intelligent satellite IoT architecture that uses the superpixel-based CNN approach. At the fog layer, the superpixel-based simple linear iterative cluster (SLIC) algorithm uses the images captured by the satellites of the edge level to produce the smaller-sized superpixel images that can be transferred even in a low bandwidth link. The CNN module at the cloud level is then trained with these superpixel images to predict the road networks from these RS images. Two popular road datasets: the DeepGlobe Road dataset and the Massachusetts Road dataset, have been considered to prove the usefulness of the proposed SLIC-CNN architecture in satellite-based IoT platforms to address the problems like RS image-based road extraction. The proposed architecture achieves better performance accuracy than the classical CNN while reducing the incurred overhead by a noticeable limit. • Presents a superpixel-inspired deep learning-based architecture that optimizes the time and memory overhead. • Presents the three-layer edge-fog-cloud-based satellite IoT architecture to deploy the proposed SLIC-CNN framework. • Discusses the efficiency of the proposed deep architecture to work in a low bandwidth environment for road segmentation. • Highlights the application areas such as emergency alarm systems that can use the architecture to operate in real-time.

Classification of Satellite Imagery for Identifying Land-Cover Objects using ECW Compression Images: The Case of Makassar City Area

This paper presents a case study on the effect of lossy compression using the Enhanced Compressed Wavelet (ECW) format on remote sensing image classification. ECW was chosen because it is widely used as a standard format for storing aerial and satellite imagery. The case study was conducted on a high-resolution multispectral Pleiades image taken from an area in Makassar, Indonesia. Image classification is performed using the geographic object-based image analysis method, where a simple linear iterative clustering (SLIC) algorithm is implemented for segmentation before classification. Six land cover categories were selected to validate the classification results: water bodies, trees, rice fields, shrubs, and urban areas. The effect of image compression on classification accuracy is studied by varying the compression ratio. Then the results are compared with the original image. Experimental results prove that compression with ECW format does not have much effect on classification accuracy. Even the Random Forest and Gradient Boosting Machine provide higher accuracy with the compressed image compared to the original image. In addition, it can be concluded that Random Forest is the best classifier with the highest accuracy.