Visual interactive image clustering:a target-independent approach for configuration optimization in machine vision measurement

Machine vision measurement(MVM)is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control.The result of MVM is determined by its configuration,especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing.In a traditional workflow,engineers constantly adjust and verify the configuration for an acceptable result,which is time-consuming and significantly depends on expertise.To address these challenges,we propose a target-independent approach,visual interactive image clustering,which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters.Our approach has four steps:data preparation,data sampling,data processing,and visual analysis with our visualization system.During preparation,engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images.Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm.The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters.Based on the image relationships,we develop VMExplorer,a visual analytics system that assists engineers in grouping images into clusters and exploring parameters.Finally,engineers can determine an appropriate lighting scheme with robust parameter combinations.To demonstrate the effiectiveness and usability of our approach,we conduct a case study with engineers and obtain feedback from expert interviews.

A novel unsupervised approach for multilevel image clustering from unordered image collection

A novel unsupervised approach to automatically constructing multilevel image clusters from unordered im- ages is proposed in this paper. The whole input image col- lection is represented as an imaging sample space （ISS） con- sisting of globally indexed image features extracted by a new efficient multi^view image feature matching method. By mak- ing an analogy between image capturing and observation of ISS, each image is represented as a binary sequence, in which each bit indicates the visibility of a corresponding feature. Based on information theory-inspired image popularity and dissimilarity measures, we show that the image content and distance can be quantitatively described, guided by which an input image collection is organized into multilevel clusters automatically. The effectiveness and the efficiency of the pro- posed approach are demonstrated using three real image col- lections and promising results were obtained from both qual- itative and quantitative evaluation.

Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

This paper presents,a new approach of Medical Image Pixels Clustering(MIPC),aims to trace the dissimilar patterns over the Magnetic Resonance(MR)image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment,pattern predication and deeper investigation.The proposed MIPC consists of two stages:clustering and validation.In the clustering stage,the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering(iLIAC),Dynamic Automatic Agglomerative Clustering(DAAC)and Optimum N-Means(ONM).In the second stage,the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure(SICM).Experimental results showthat the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Multiple Hypergraph Clustering of Web Images by Mining Word2Image Correlations

In this paper, we consider the problem of clustering Web images by mining correlations between images and their corresponding words. Since Web images always come with associated text, the corresponding textual tags of Web images are used as a source to enhance the description of Web images. However, each word has different contribution for the interpretation of image semantics. Therefore, in order to evaluate the importance of each corresponding word of Web images, we propose a novel visibility model to compute the extent to which a word can be perceived visually in images, and then infer the correlation of word to image by the integration of visibility with tf-idf. Furthermore, Latent Dirichlet Allocation （LDA） is used to discover topic information inherent in surrounding text and topic correlations of images could be defined for image clustering. For integrating visibility and latent topic information into an image clustering framework, we first represent textual correlated and latent-topic correlated images by two hypergraph views, and then the proposed Spectral Multiple Hypergraph Clustering （SMHC） algorithm is used to cluster images into categories. The SMHC could be regarded as a new unsupervised learning process with two hypergraphs to classify Web images. Experimental results show that the SMHC algorithm has better clustering performance and the proposed SMHC-based image clustering framework is effective.

PDGI-BASED REGULAR SWEPT SURFACE EXTRACTION FROM POINT CLOUD

A principal direction Gaussian image （PDGI）-based algorithm is proposed to extract the regular swept surface from point cloud. Firstly, the PDGI of the regular swept surface is constructed from point cloud, then the bounding box of the Gaussian sphere is uniformly partitioned into a number of small cubes （3D grids） and the PDGI points on the Gaussian sphere are associated with the corresponding 3D grids. Secondly, cluster analysis technique is used to sort out a group of 3D grids containing more PDGI points among the 3D grids. By the connected-region growing algorithm, the congregation point or the great circle is detected from the 3D grids. Thus the translational direction is determined by the congregation point and the direction of the rotational axis is determined by the great circle. In addition, the positional point of the rotational axis is obtained by the intersection of all the projected normal lines of the rotational surface on the plane being perpendicular to the estimated direction of the rotational axis. Finally, a pattem search method is applied to optimize the translational direction and the rotational axis. Some experiments are used to illustrate the feasibility of the above algorithm.

A Discriminative Algorithm for Indoor Place Recognition Based on Clustering of Features and Images

In order to solve the problem of indoor place recognition for indoor service robot, a novel algorithm, clustering of features and images （CFI）, is proposed in this work. Different from traditional indoor place recognition methods which are based on kernels or bag of features, with large margin classifier, CFI proposed in this work is based on feature matching, image similarity and clustering of features and images. It establishes independent local feature clusters by feature cloud registration to represent each room, and defines image distance to describe the similarity between images or feature clusters, which determines the label of query images. Besides, it improves recognition speed by image scaling, with state inertia and hidden Markov model constraining the transition of the state to kill unreasonable wrong recognitions and achieves remarkable precision and speed. A series of experiments are conducted to test the algorithm based on standard databases, and it achieves recognition rate up to 97% and speed is over 30 fps, which is much superior to traditional methods. Its impressive precision and speed demonstrate the great discriminative power in the face of complicated environment.