Cluster Appearance Glyphs: A Methodology for Illustrating High-Dimensional Data Patterns in 2-D Data Layouts

Two-dimensional space embeddings such as Multi-Dimensional Scaling (MDS) are a popular means to gain insight into high-dimensional data relationships. However, in all but the simplest cases these embeddings suffer from significant distortions, which can lead to misinterpretations of the high-dimensional data. These distortions occur both at the global inter-cluster and the local intra-cluster levels. The former leads to misinterpretation of the distances between the various N-D cluster populations, while the latter hampers the appreciation of their individual shapes and composition, which we call cluster appearance. The distortion of cluster appearance incurred in the 2-D embedding is unavoidable since such low-dimensional embeddings always come at the loss of some of the intra-cluster variance. In this paper, we propose techniques to overcome these limitations by conveying the N-D cluster appearance via a framework inspired by illustrative design. Here we make use of Scagnostics which offers a set of intuitive feature descriptors to describe the appearance of 2-D scatterplots. We extend the Scagnostics analysis to N-D and then devise and test via crowd-sourced user studies a set of parameterizable texture patterns that map to the various Scagnostics descriptors. Finally, we embed these N-D Scagnostics-informed texture patterns into shapes derived from N-D statistics to yield what we call Cluster Appearance Glyphs. We demonstrate our framework with a dataset acquired to analyze program execution times in file systems.

A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching

Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.

Cube of Space Sampling for 3D Model Retrieval

Since the number of 3D models is rapidly increasing, extracting better feature descriptors to represent 3D models is very challenging for effective 3D model retrieval. There are some problems in existing 3D model representation approaches. For example, many of them focus on the direct extraction of features or transforming 3D models into 2D images for feature extraction, which cannot effectively represent 3D models. In this paper, we propose a novel 3D model feature representation method that is a kind of voxelization method. It is based on the space-based concept, namely CSS (Cube of Space Sampling). The CSS method uses cube space 3D model sampling to extract global and local features of 3D models. The experiments using the ESB dataset show that the proposed method to extract the voxel-based features can provide better classification accuracy than SVM and comparable retrieval results using the state-of-the-art 3D model feature representation method.

A New Optimized Hybrid Local Lifting Wavelet Co-occurrence Texture Pattern for Content Based Medical Image Retrieval

<p class="0abstract">Medical image retrieval (MIR) is a hard task owing to the varied patterns and structures in the medical images. The feature descriptors have been used to describe the images in most MIR approaches. Based on the local relationship, several feature descriptors of neighbouring image pixels have been proposed for MIR so far, but their low performance scores make them unsuitable. In this paper, an efficient optimized hybrid local lifting wavelet co-occurrence texture pattern for content-based MIR is proposed. Initially, image resize and Adaptive histogram equalization technique is used to carried out for contrast enhancement. Then Local Lifting Wavelet Co-occurrence Texture Pattern is derived using Local tetra pattern, Gradient directional pattern, lifting wavelet transform and Gray level co-occurrence matrix. An Equilibrium optimization technique is employed to select the most important features of an image from the obtained feature vectors (FV). Finally, to match the query image with the database images, distance between their FV is computed and the minimum distance images are considered as retrieval outcome. Three benchmark medical databases of various modalities (CT and MRI) are used to test the efficiency of the proposed method: EXACT-09, TCIA-CT, and OASIS. The experimental results prove that the proposed approach outperforms existing descriptors in terms of APR and ARR.</p>

GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene

Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.

Deep learning model for protein disease classification

Background: Protein sequence analysis helps in the prediction of protein functions. As the number of proteins increases, it gives the bioinformaticians a challenge to analyze and study the similarity between them. Most of the existing protein analysis methods use Support Vector Machine. Deep learning did not receive much attention regarding protein analysis as it is noted that little work focused on studying the protein diseases classification. Objective: The contribution of this paper is to present a deep learning approach that classifies protein diseases based on protein descriptors. Methods: Different protein descriptors are used and decomposed into modified feature descriptors. Uniquely, we introduce using Convolutional Neural Network model to learn and classify protein diseases. The modified feature descriptors are fed to the Convolutional Neural Network model on a dataset of 1563 protein sequences classified into 3 different disease classes: Aids, Tumor suppressor, and Proto oncogene. Results: The usage of the modified feature descriptors shows a significant increase in the performance of the Convolutional Neural Network model over Support Vector Machine using different kernel functions. One modified feature descriptor improved by 19.8%, 27.9%, 17.6%, 21.5%, 17.3%, and 22% for evaluation metrics: Area Under the Curve, Matthews Correlation Coefficient, Accuracy, F1-score, Recall, and Precision, respectively. Conclusion: Results show that the prediction of the proposed modified feature descriptors significantly surpasses that of Support Vector Machine model.