Descriptor Vector Research Articles

Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm

Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.

3D CAD model retrieval based on sketch and unsupervised variational autoencoder

How to quickly, accurately retrieve and effectively reuse 3D CAD models that conform to user’s design intention has become an urgent problem in product design. However, there are several problems with the existing retrieval methods, like not being fast, or accurate, or hard to use. Hence it is difficult to meet the actual needs of the industry. In this paper, we propose a 3D CAD model retrieval approach that considers the speed, accuracy and ease of use at the same time, based on sketches and unsupervised learning. Firstly, the loop is used as the fundamental element of sketch/view, and automatic structural semantics capture algorithms are proposed to extract and construct attributed loop relation tree; Secondly, the recursive neural network based deep variational autoencoders is constructed and optimized to transform arbitrary shapes and sizes of loop relation tree into fixed length descriptor; Finally, based on the fixed length vector descriptor, the sketches and views of 3D CAD models are embedded into the same target feature space, and k-nearest neighbors algorithm is adopted to conduct fast CAD model matching on the feature space. In this manner, a prototype 3D CAD model retrieval system is developed. Experiments on the dataset containing about two thousand 3D CAD models validate the feasibility and effectiveness of the proposed approach.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-Based Image Retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results in the order 95.4%, 88.2%, 84.4% and 96.05% for Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depicts an experimental and analytical analysis on different color feature descriptors namely, Color Moment (CM), Color Auto-Correlogram (CAC), Color Histogram (CH), Color coherence vector (CCV) and Dominant Color Descriptor (DCD). The proposed hybrid Color Descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade Forward Back Propagation Neural Network (CFBPNN) used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

Efficient coarse registration method using translation- and rotation-invariant local descriptors towards fully automated forest inventory

In this paper, we present a simple, efficient, and robust algorithm for 2D coarse registration of two point clouds. In the proposed algorithm, the locations of some distinct objects are detected from the point cloud data, and a rotation- and translation-invariant feature descriptor vector is computed for each of the detected objects based on the relative locations of the neighboring objects. Subsequently, the feature descriptors obtained for the different point clouds are compared against one another by using the Euclidean distance in the feature space as the similarity criterion. By using the nearest neighbor distance ratio, the most promising matching object pairs are found and further used to fit the optimal Euclidean transformation between the two point clouds. Importantly, the time complexity of the proposed algorithm scales quadratically in the number of objects detected from the point clouds. We demonstrate the proposed algorithm in the context of forest inventory by performing coarse registration between terrestrial and airborne point clouds. To this end, we use trees as the objects and perform the coarse registration by using no other information than the locations of the detected trees. We evaluate the performance of the algorithm using both simulations and three test sites located in a boreal forest. We show that the algorithm is fast and performs well for a large range of stem densities and for test sites with up to 10 ​000 trees. Additionally, we show that the algorithm works reliably even in the case of moderate errors in the tree locations, commission and omission errors in the tree detection, and partial overlap of the data sets. We also demonstrate that additional tree attributes can be incorporated into the proposed feature descriptor to improve the robustness of the registration algorithm provided that reliable information of these additional tree attributes is available. Furthermore, we show that the registration accuracy between the terrestrial and airborne point clouds can be significantly improved if stem positions estimated from the terrestrial data are matched to stem positions obtained from the airborne data instead of matching them to tree top positions estimated from the airborne data. Even though the 2D coarse registration algorithm is demonstrated in the context of forestry, the algorithm is not restricted to forest data and it may potentially be utilized in other applications, in which efficient 2D point set registration is needed.

SSSGAN: Satellite Style and Structure Generative Adversarial Networks

This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area.

Laser-based intelligent perception method for tunnel invasion

Laser-based visual perception technology is an effective way to enhance the intelligent level of tunnel operation and maintenance, which could improve the efficiency of the automatic identification and intelligent positioning of different diseases. Although various sensing and data processing methods for tunnel inspection have been studied, the research on invasion detection based on laser scanning technology is still rare. In this paper, the invasion detection of the tunnel and train is carried out based on terrestrial laser scanning data. A feature-based registration is proposed in order to fuze the tunnel and the train models accurately and automatically in the unified coordinate system, where the local contact points are set as the features and identified by means of the normal vector descriptor. The invasion analysis is achieved with the convex points, which are paired through the convex hull and polar principle. A complete and comprehensive image of the invasion analysis is generated, which offers a reality model base for the design and decision-making. The method proposed can be extended to other similar scale scenarios of invasion analysis and spatial position inspection. It is a reference for the engineering applications of the intelligent operation and maintenance of the tunnel.

UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.

Assistant human facial recognition based on convolution feature-map

Face recognition is a very important research direction in the field of image processing. In daily life, there are various problems that require face recognition everywhere, but how to accurately identify faces from images is a very important and urgent problem. To this end, we propose an assistant face recognition method that can effectively improve the recognition accuracy. The face recognition method based on deep learning will convolve the input image into five different convolution kernels to obtain the convolution feature-map of each layer. At the end, the corresponding description vector of the input image is generated by using the two layers fully connected layer and the pooling layer. Therefore, in the process of face recognition, it is only necessary to calculate the similarity between the description vectors of the two frames of images. Because of its recognition accuracy and room for improvement, we use the features of the fifth-level convolution feature-map, combined with the image description vector finally generated by the deep learning method, can significantly improve the recognition accuracy in the face recognition process. Through a large number of experiments, the method can be used to assist the image recognition problem based on the deep learning method, which can effectively improve the recognition accuracy.

IsoExplorer: an isosurface-driven framework for 3D shape analysis of biomedical volume data.

The high-resolution scanning devices developed in recent decades provide biomedical volume datasets that support the study of molecular structure and drug design. Isosurface analysis is an important tool in these studies, and the key is to construct suitable description vectors to support subsequent tasks, such as classification and retrieval. Traditional methods based on handcrafted features are insufficient for dealing with complex structures, while deep learning-based approaches have high memory and computation costs when dealing directly with volume data. To address these problems, we propose IsoExplorer, an isosurface-driven framework for 3D shape analysis of biomedical volume data. We first extract isosurfaces from volume data and split them into individual 3D shapes according to their connectivity. Then, we utilize octree-based convolution to design a variational autoencoder model that learns the latent representations of the shape. Finally, these latent representations are used for low-dimensional isosurface representation and shape retrieval. We demonstrate the effectiveness and usefulness of IsoExplorer via isosurface similarity analysis, shape retrieval of real-world data, and comparison with existing methods.Graphic abstract

Feature Matching Method Based on SURF and Fast Library for Approximate Nearest Neighbor Search

Aiming at dimensional feature vector matching problem of low accuracy, a kind of image matching algorithm is based on SURF and fast library for approximate nearest neighbor search. Fast-Hessian was used to detect the feature points, and the SURF feature description vector proposed was generated; through fast library for approximate nearest neighbor search algorithm, pre-matching point was got. With the introduction of Random Sample Consensus (RANSAC) algorithm points false-matching, based on the SIFT algorithm, the result of SURF algorithm and optimization algorithm was proposed and mismatching points were eliminated after the experiment simulation. Experimental results show that at the same time the matching algorithm improve matching accuracy rate, and the algorithm of the real-time performance is also improved.

HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

Automation of some macromolecular properties using a machine learning approach

In this study, we employed a newly developed method to predict macromolecular properties using a swarm artificial neural network (ANN) method as a machine learning approach. In this method, the molecular structures are represented by the feature description vectors used as training input data for a neural network. This study aims to develop an efficient approach for training an ANN using either experimental or quantum mechanics data. We aim to introduce an error model controlling the reliability of the prediction confidence interval using a bootstrapping swarm approach. We created different datasets of selected experimental or quantum mechanics results. Using this optimized ANN, we hope to predict properties and their statistical errors for new molecules. There are four datasets used in this study. That includes the dataset of 642 small organic molecules with known experimental hydration free energies, the dataset of 1475 experimental pKa values of ionizable groups in 192 proteins, the dataset of 2693 mutants in 14 proteins with given experimental values of changes in the Gibbs free energy, and a dataset of 7101 quantum mechanics heat of formation calculations. All the data are prepared and optimized using the AMBER force field in the CHARMM macromolecular computer simulation program. The bootstrapping swarm ANN code for performing the optimization and prediction is written in Python computer programming language. The descriptor vectors of the small molecules are based on the Coulomb matrix and sum over bond properties. For the macromolecular systems, they consider the chemical-physical fingerprints of the region in the vicinity of each amino acid.

Pairwise registration for terrestrial laser scanner point clouds based on the covariance matrix

ABSTRACT Automatic pairwise registration for unordered terrestrial laser scanner (TLS) point clouds is the pre-requisite for many applications, including 3D model reconstruction, cultural heritage management and landslide monitoring. However, most of the existing registration methods are still suffering from several limitations: (1) difficult to find the correct corresponding point pairs from the source and target point cloud, (2) low accuracy of pairwise registration and (3) the iterative process of registration is time-consuming. To overcome these challenges, based on the covariance matrix, this paper presents a robust and descriptive feature descriptor vector (FDV) to locally describe a point, by which the corresponding point pairs are obtained and the registration matrix is calculated. First, the FDVs of the original point cloud’s keypoints are calculated. Second, the corresponding point pairs are found via their FDVs. Third, the coarse transformation matrix is calculated by the corresponding point pairs using the singular value decomposition (SVD) algorithm, and which is further refined by the iterative closest point (ICP) algorithm to get a better result. Finally, the experiments are conducted on the Autonomous Systems Lab (ASL) and ISPRS datasets; our results show that the proposed algorithm can obtain good registration performance, and outperforms the compared methods.

Predicting changes in substance use following psychedelic experiences: natural language processing of psychedelic session narratives

ABSTRACT Background: Experiences with psychedelic drugs, such as psilocybin or lysergic acid diethylamide (LSD), are sometimes followed by changes in patterns of tobacco, opioid, and alcohol consumption. But, the specific characteristics of psychedelic experiences that lead to changes in drug consumption are unknown. Objective: Determine whether quantitative descriptions of psychedelic experiences derived using Natural Language Processing (NLP) would allow us to predict who would quit or reduce using drugs following a psychedelic experience. Methods: We recruited 1141 individuals (247 female, 894 male) from online social media platforms who reported quitting or reducing using alcohol, cannabis, opioids, or stimulants following a psychedelic experience to provide a verbal narrative of the psychedelic experience they attributed as leading to their reduction in drug use. We used NLP to derive topic models that quantitatively described each participant’s psychedelic experience narrative. We then used the vector descriptions of each participant’s psychedelic experience narrative as input into three different supervised machine learning algorithms to predict long-term drug reduction outcomes. Results: We found that the topic models derived through NLP led to quantitative descriptions of participant narratives that differed across participants when grouped by the drug class quit as well as the long-term quit/reduction outcomes. Additionally, all three machine learning algorithms led to similar prediction accuracy (~65%, CI = ±0.21%) for long-term quit/reduction outcomes. Conclusions: Using machine learning to analyze written reports of psychedelic experiences may allow for accurate prediction of quit outcomes and what drug is quit or reduced within psychedelic therapy.

Ensemble Visual Content Based Search and Retrieval for Natural Scene Images

Background: Content Based Image Retrieval (CBIR) is one of the fields for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, colour, shape, and other information. Objective: The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. Methods: In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the precision and recall for the gained results using single content descriptor is not significant. The proposed system architecture uses the hybrid ensemble feature set for image matching. The hybrid parameters include globally and locally defined feature extraction methodologies. Results: The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes. The overall combination will provide almost 97% accurate results which is better than the existing literature results. Conclusion: In conclusion the experimentation result suggests that the use of the local feature extraction mechanism is better when compared with the global feature extraction methodology.

Learning representative temporal features for action recognition

In this paper, a novel video classification method is presented that aims to recognize different categories of third-person videos efficiently. Our motivation is to achieve a light model that could be trained with insufficient training data. With this intuition, the processing of the 3-dimensional video input is broken to 1D in temporal dimension on top of the 2D in spatial. The processes related to 2D spatial frames are being done by utilizing pre-trained networks with no training phase. The only step which involves training is to classify the 1D time series resulted from the description of the 2D signals. As a matter of fact, optical flow images are first calculated from consecutive frames and described by pre-trained CNN networks. Their dimension is then reduced using PCA. By stacking the description vectors beside each other, a multi-channel time series is created for each video. Each channel of the time series represents a specific feature and follows it over time. The main focus of the proposed method is to classify the obtained time series effectively. Towards this, the idea is to let the machine learn temporal features. This is done by training a multi-channel one dimensional Convolutional Neural Network (1D-CNN). The 1D-CNN learns the features along the only temporal dimension. Hence, the number of training parameters decreases significantly which would result in the trainability of the method on even smaller datasets. It is illustrated that the proposed method could reach the state-of-the-art results on two public datasets UCF11, jHMDB and competitive results on HMDB51.

Practical Estimation of Landslide Kinematics Using PSI Data

Kinematics is a key component of a landslide hazard because landslides moving at similar rates can affect structures or collapse differently depending on their mechanisms. While a complete definition of landslide kinematics requires integrating surface and subsurface site investigation data, its practical estimate is usually based on 2D profiles of surface slope displacements. These can be now measured accurately using Persistent Scatterer InSAR (PSI), which exploits open access satellite imagery. Although 2D profiles of kinematic quantities are easy to retrieve, the efficacy of possible descriptors and extraction strategies has not been systematically compared, especially for complex landslides. Large, slow rock slope deformations, characterized by low displacement rates (<50 mm/year) and spatial and temporal heterogeneities, are an excellent testing ground to explore the best approaches to exploit PSI data from Sentinel-1 for kinematic characterization. For three case studies, we extract profiles of different kinematic quantities using different strategies and evaluate them against field data and simplified numerical modelling. We suggest that C-band PSI data allow for an effective appraisal of complex landslide kinematics, provided that the interpretation is (a) based on decomposed velocity vector descriptors, (b) extracted along critical profiles using interpolation techniques respectful of landslide heterogeneity, and (c) constrained by suitable model-based templates and field data.

Efficient and high-performance pedestrian detection implementation for intelligent vehicles

Implementing pedestrian detection real-time embedded systems remains a major challenge. Detecting pedestrians in an advanced driver assistance system requires a lot of time and resources. The method based on Multi-Scale Region Covariance Descriptor (MSRCD) and Support Vector Machine (SVM) is one of the most effective approaches to perform pedestrian detection. However, such implementation is difficult to be executed in real time on embedded systems. This paper presents three improvements to adapt the solution based on the MSRCD descriptor and SVM classifier for embedded pedestrian detection. First, a new feature combination capable to provide the most accurate description at a minimum processing time for MSRCD is proposed. Second, to speed up the SVM classification, a new approach that adopts the mean SVM technique and Euclidian distance is proposed. Third, parallel implementation is exploited to accelerate processing time on multi-core architectures. The software implementation is performed using the INRIA data set. 18.94% processing time speed-up, 48.21% less memory usage, and 2.38% improved detection accuracy are achieved using the proposed descriptor. Moreover, 58.22% processing time speed-up is obtained for the proposed classifier while keeping the same testing accuracy. The parallel implementation is performed using dual ARM Cortex-A9 processors of a selected Xilinx zynq platform. The obtained results confirmed the effectiveness of the proposed parallelization at accelerating the computing time about 3 times the original sequential processing.

Visual Positioning in Indoor Environments Using RGB-D Images and Improved Vector of Local Aggregated Descriptors

Positioning information has become one of the most important information for processing and displaying on smart mobile devices. In this paper, we propose a visual positioning method using RGB-D image on smart mobile devices. Firstly, the pose of each image in the training set is calculated through feature extraction and description, image registration, and pose map optimization. Then, in the image retrieval stage, the training set and the query set are clustered to generate the vector of local aggregated descriptors (VLAD) description vector. In order to overcome the problem that the description vector loses the image color information and improve the retrieval accuracy under different lighting conditions, the opponent color information and depth information are added to the description vector for retrieval. Finally, using the point cloud corresponding to the retrieval result image and its pose, the pose of the retrieved image is calculated by perspective-n-point (PnP) method. The results of indoor scene positioning under different illumination conditions show that the proposed method not only improves the positioning accuracy compared with the original VLAD and ORB-SLAM2, but also has high computational efficiency.

Half-edge composite structure: good performance in motion matching

In this paper, a method of half-edge composite structure (HECS) extraction is proposed. First, an edge is divided into two half-edges, and then one half-edge is combined to form a HECS according to the condition. A HECS is an infrastructure composed of two half-edges and one endpoint. Matching means to calculate the distance between the HECS description vectors of two images and to get high-quality matching according to the conditions. To describe a more complete image structure, we propose a graph structure combination method based on a HECS. The feature set of the graph structure is formed by the association of the HECS, and the matching relationship of the graph structure set is mapped by the matching results of the HECS. Based on the image structure set and visual residual phenomenon, a motion matching model of video stream is constructed, which is called the visual residual model. For numerical estimation of natural images, we propose an evaluation method to investigate the feature stability of video images. The result shows that performance of the algorithm discussed in this paper has a good performance in the numerical estimation of continuous matching feature stability shown in the video stream.