Content-Based Video Retrieval Using Integration of Curvelet Transform and Simple Linear Iterative Clustering

This paper investigates the application of the Simple Linear Iterative Clustering (SLIC) algorithm for complex object image segmentation, on the example of images of human body injuries. The study solves the problem of the lack of quantitative evidence regarding SLIC's algorithm performance in high-precision area and boundary assessment of the lesion on a digital image of a human body with a wound injury on it. A comprehensive methodology is developed to evaluate SLIC's algorithm efficacy across various complex images and image resolutions. The research utilizes a combined dataset of 3696 wound images from the Foot Ulcer Segmentation Challenge (FUSeg) and WoundSeg datasets. Bayesian optimization is utilized to fine-tune SLIC algorithm hyperparameters, focusing on the number of segments and compactness. Results indicate that SLIC algorithm demonstrates consistent performance across different implementations, achieving Dice scores around 0.84 and Soft Boundary F1 scores around 0.55. The study reveals that the optimal number of segments for SLIC algorithm can be defined relative to the spatial dimensions of the input image, with maximal image dimension *2 being the most effective value. A thorough analysis of various segmentation metrics is conducted, including IoU, Dice Score, and Boundary F1 Score. The research introduces and employs the Soft Boundary F1 Score – modification of Boundary F1 Score, a novel metric designed to provide a more nuanced evaluation of boundary detection accuracy while offering a smoother optimization landscape. This metric proves particularly valuable in assessing the performance of SLIC algorithm in image with complex objects on them segmentation tasks. Importantly, this research presents an idealized SLIC-based segmentation approach, where superpixels are optimally combined using ground-truth masks to establish an upper bound of performance. This idealized SLIC algorithm segmentation is compared with a pre-trained on the FUSeg dataset UNet model, showcasing superior generalization capability across diverse wound types. On the WoundSeg dataset, the idealized SLIC algorithm approach achieved a Dice score of 0.84, significantly outperforming the UNet model (0.12 Dice score). As a result, this study provides valuable insights for improving complex objects segmentation methods and highlights the need for further research on developing effective methods for superpixel classification in real-world scenarios. The findings also highlight the potential of SLIC-based approaches in addressing the challenges of diverse data types and limited training data.

Simple Linear Iterative Clustering (SLIC) algorithm is increasingly applied to different kinds of image processing because of its excellent perceptually meaningful characteristics. In order to better meet the needs of medical image processing and provide technical reference for SLIC on the application of medical image segmentation, two indicators of boundary accuracy and superpixel uniformity are introduced with other indicators to systematically analyze the performance of SLIC algorithm, compared with Normalized cuts and Turbopixels algorithm. The extensive experimental results show that SLIC is faster and less sensitive to the image type and the setting superpixel number than other similar algorithms such as Turbopixels and Normalized cuts algorithms. And it also has a great benefit to the boundary recall, the robustness of fuzzy boundary, the setting superpixel size and the segmentation performance on medical image segmentation.

For better analyzing and adopting superpixel methods in image segmentation, an improved Simple Linear Iterative Clustering (SLIC) algorithm is put forward, which sustains that homogeneous regions show high consistence during clustering. Firstly, a primary iteration of SLIC approach is employed to generate uniform superpixels. Then an update strategy is introduced to determine whether to recluster the candidate region. In homogeneous regions, a cluster center often shows relatively small deviation of last position in spatial-color feature space when local iteration occurs, which can be regarded as redundant computation and be reduced. Experimental results show that the strategy brings a remarkable increased efficiency for traditional SLIC without significant affection on segmentation results.

Road detection aims at detecting the (drivable) road surface ahead vehicle and plays a crucial role in driver assistance system. To improve the accuracy and robustness of road detection approaches in complex environments, a new road detection method based on CNN (convolutional neural network) and MRF (markov random field) is proposed. The original road image is segmented into super-pixels of uniform size using simple linear iterative clustering (SLIC) algorithm. On this basis, we train the CNN which can automatically learn the features that are most beneficial to classification. Then, the trained CNN is applied to classify road region and non-road region. Finally, based on the relationship between the super-pixels neighborhood, we utilize MRF to optimize the classification results of CNN. Quantitative and qualitative experiments on the publicly datasets demonstrate that the proposed method is robust in complex environments. Furthermore, compared with state-of-the-art algorithms, the approach provides the better performance.

Registration between the baseline and follow-up lung computed tomography (CT) volumes plays an important role in computer-aided diagnosis and following-up care during adaptive radiotherapy. Diffeomorphic log-Demons as state of the art in Demons implementations is restricted to relatively small deformations, low accuracy, and ignoring some prior structural features. In this paper, an automated superpixel-borders-guided deformation image registration (SBG-DIR) algorithm is proposed. The proposed SBG-DIR method uses the simple linear iterative clustering (SLIC) algorithm to automated superpixels generation. Incorporation of superpixel borders into registration algorithm is implemented by a new similarity criterion based on the binary volume representation of superpixel borders. The binary volume representation enables accurate preserving motion boundaries, contributes to a faster convergence of the objective function and eliminates errors caused by manual interaction. In addition, a subtraction volume is produced by the intensity difference between the first time point CT volume and its warped follow-up CT volume. The subtraction volume can be used for detection of tumor tissue growth or shrinkage, which is an essential part of a CT-based diagnosis. Moreover, to ensure the topology preservation of biological objects, our proposed SBG-DIR method is implemented in the space of diffeomorphisms, in which meaningful biological shapes can be found. Compared with the state-of-the-art Demons, the proposed SBG-DIR method doesn’t require any additional optimization, yields a faster convergence and is more accurate and efficient in recovering large deformations. Experimental results indicate that the proposed SBG-DIR method performed better than the state-of-the-art Demons algorithms.

Over-segmentation, or superpixel segmentation is widely applied in various image processing tasks such as image classification and region-based change detection. In polarimetric synthetic aperture radar (PolSAR) image processing, the complex Wishart distribution is commonly used in the modeling of homogeneous regions. In this paper, we introduce a segmentation method for multi-temporal PolSAR images based on the simple linear iterative clustering (SLIC) framework. We apply the Wishart distribution-based distance and modify the combination form for the SLIC distance measure such that the SLIC method can be adapted to the statistical characteristics of PolSAR imagery. Compared with existing PolSAR image superpixel methods, the proposed method has better time cost efficiency while maintaining the performance on segmentation results. Moreover, the proposed method is capable of jointly segmenting two or more multi-temporal PolSAR images, which is the basis for some further earth observation applications such as change detection and region-based data fusion tasks.

ABSTRACTThe proposed content-based video retrieval method in this study uses feature extraction, shot boundary detection, shot noise filter, shot cluster, the key feature set of shot extraction and shot classification. Shot boundary detection is conducted using a weight selection genetic algorithm. In order to verify the video retrieval method in this paper, three video databases were used in the experiment. These video databases are used to verify the shot boundary detection, the shot classification and video retrieval of this study. A series of comparisons and analyses were then conducted using the above video databases. The experiment results showed that the shot boundary detection method exhibits better performance than the other compared methods. Moreover, the video retrieval experiment results showed that the proposed method is able to precisely catch a shot in the search video, and do so with reduced video retrieval time.

In view interpolation, information missing often exists in initial depth map, moreover, disocclusion regions usually occur along the foreground object boundaries after 3D warping. Generally, initial depth map and warped depth map have a strong influence on the performance of view interpolation. However, most of existing view interpolation algorithms only emphasize hole filling of the warped color image. In this paper, a superpixel-based method is proposed for initial depth map enhancement and warped depth map hole filling. Firstly, the color image is segmented using simple linear iterative clustering (SLIC) algorithm, and after that, the associated depth map is segmented with the same label. Then, the depthmissing pixels are recovered by considering color and depth superpixel information jointly. Additionally, holes of the disocclusion regions in the warped depth map can also be filled efficiently via superpixel-based segmentation. Experimental results show that with the proposed method the quality of the interpolated view has been improved significantly in terms of both subjective and objective evaluations.

The water body extraction technology based on superpixel segmentation in SAR images faces challenges such as insufficient precision in extracting fine water boundaries under speckle noise and complex scattering conditions. To address these issues, this paper proposes an enhanced fine water body extraction method based on superpixel segmentation for SAR images. In the superpixel segmentation phase, a simple linear iterative clustering (SLIC) superpixel segmentation algorithm based on eightdirection convolution (referred to as EDC-SLIC algorithm) is introduced. This algorithm constructs three pseudo-channels using eight-direction convolution to replace the three color channels of traditional color images and employs logarithmic difference measurement in the color distance calculation part of the SLIC algorithm, thereby adapting it to the segmentation requirements of SAR images. In the water body information extraction phase, a multi-feature weighted Otsu water body information extraction algorithm integrating superpixels (referred to as MFW-Otsu algorithm) is proposed. This algorithm integrates local mean and variance into a new feature image through weighting, enabling more accurate representation of texture changes in the image and enhancing the algorithm's ability to process complex image structures. The experimental results demonstrate that the EDC-SLIC algorithm and MFW-Otsu algorithm exhibit significant advantages in accuracy, robustness, and practicality. Furthermore, the integration of superpixels effectively improves the algorithm's adaptability to complex scenes, enhances detail processing capabilities, reduces misclassification phenomena, and improves the accuracy of water body information extraction.

Alzheimer's Disease (AD) poses many challenges to healthcare systems for elderly people in the world, for which an accurate diagnosis of AD is therefore a crucial task. Positron Emission Tomography (PET) images and machine learning can be used to support this purpose. Brain atlases are common tools used to extract features from PET images, and choosing a proper brain map is extremely important. In this work, we propose a novel method to learn the very first PET-driven brain mapping based on Support Vector Machine (SVM) coefficients and Simple Linear Iterative Clustering (SLIC) algorithm, which is addressed as C-Atlas. When compared with predefined atlases on classification task, C-atlas experimentally proved to be more adapted for AD diagnosis both in terms of feature extraction and selection. The results suggest that it is possible to build an adapted brain mapping for a specific disease, and that the proposed method can also be used as an effective mean to support the analysis and comparison of brain diseases at multiple scales.

Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel. At present, there is no method to evaluate the quality of superpixel classification. To solve the above problems, this paper proposes a hybrid classification model based on superpixel entropy discrimination (SED), and constructs a two-level cascade classifier. Firstly, a light gradient boosting machine (LGBM) was used to process large-dimensional input features, and simple linear iterative clustering (SLIC) was integrated to obtain the primary classification results based on superpixels. Secondly, information entropy was introduced to evaluate the quality of superpixel classification, and a complex-valued convolutional neural network (CV-CNN) was used to reclassify the high-entropy superpixels to obtain the secondary classification results. Experiments with two measured PolSAR datasets show that the overall accuracy of both classification methods exceeded 97%. This method suppressed the evidence conflict in a single superpixel and the inaccuracy of superpixel segmentation. The test time of our proposed method was shorter than that of CV-CNN, and using only 55% of CV-CNN test data could achieve the same accuracy as using CV-CNN for the whole image.

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative clustering for pre-segmentation procedure. The first step, we use simple linear iterative clustering algorithm to divide the image into a number of homogeneous over-segmented regions. Then, these regions are regarded as nodes, and a similarity matrix is constructed by comparing the histograms of each two regions. Finally, we apply the Ncut method to merging the over-segmented regions, then the image segmentation process is completed. The results show that the proposed segmentation scheme handles the strong speckle noise, low contrast, and weak edges well in ultrasound image. Our method has high segmentation precision and computation efficiency than the pixel-based Ncut method.

PolSAR image segmentation based on feature extraction and data compression using Weighted Neighborhood Filter Bank and Hidden Markov random field-expectation maximization

Proposal generation method for object detection in infrared image