Minimum Spanning Forest Research Articles

A Career Recommendation Method for College Students Based on Occupational Values

The numerous recruitment information and the information asymmetry between college majors and work posts make it difficult for college students to seize the job opportunities that conform to their occupational values, and the employment success rate is always at a low level. The effect of current college student career recommendation systems is usually unsatisfactory, and the existing systems haven’t fully considered the role of college students’ occupational values in instructing their employment. To fill in this research blank, this paper studied a career recommendation method for college students based on occupational values. At first, the paper proposed a collaborative filtering algorithm based on the features of collect students’ occupational values, introduced a few features that can affect their occupational values, assigned weight values to these features, and gave the method for determining the weight. Then, based on the principle of the Kruskal’s algorithm (the minimum spanning forest), this paper modified the K-Means algorithm and clustered the features of occupational values. At last, the paper elaborated in detail the principle of generating career recommendation results for college students based on occupational values, and used experimental results to verify the correctness of the proposed career recommendation algorithm.

Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms

Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms

A new spectral and spatial framework for detecting buildings with special roofing in hyperspectral images

One of the analyses in hyperspectral images is target detection. With recent developments and the creation of high spatial resolution images, the need to use both spectral and spatial information in the target detection has increased. Recently, an effective approach for spectral and spatial classification has been proposed using minimum spanning forest (MSF) algorithm. It was attempted to improve this approach for target detection in hyperspectral images. In the proposed method, the spectral image was primarily segmented using the watershed algorithm. Afterwards, for the objects resulting from segmentation, five spatial properties of area, environment, strength, meaning intensity, and entropy were extracted. Finally, the detection operation was performed utilizing the marker-based MSF algorithm. The above-mentioned techniques were applied to three hyperspectral images, first Toulouse, second Toulouse and Quebec. The results of quantitative and qualitative evaluations showed that the proposed method improved the kappa coefficient by 40, 34 and 23% in comparison with the spectral angle measurement (SAM) algorithm in the first Toulouse, second Toulouse and Quebec images, respectively.

Relaxing the Irrevocability Requirement for Online Graph Algorithms

Online graph problems are considered in models where the irrevocability requirement is relaxed. We consider the Late Accept model, where a request can be accepted at a later point, but any acceptance is irrevocable. Similarly, we consider a Late Reject model, where an accepted request can later be rejected, but any rejection is irrevocable (this is sometimes called preemption). Finally, we consider the Late Accept/Reject model, where late accepts and rejects are both allowed, but any late reject is irrevocable. We consider four classical graph problems: For Maximum Independent Set, the Late Accept/Reject model is necessary to obtain a constant competitive ratio, for Minimum Vertex Cover the Late Accept model is sufficient, and for Minimum Spanning Forest the Late Reject model is sufficient. The Maximum Matching problem admits constant competitive ratios in all cases. We also consider Maximum Acyclic Subgraph and Maximum Planar Subgraph, which exhibit patterns similar to Maximum Independent Set.

Multiple-Edge-Fault-Tolerant Approximate Shortest-Path Trees

Let G be an n-node and m-edge positively real-weighted undirected graph. For any given integer \(f \ge 1\), we study the problem of designing a sparse f-edge-fault-tolerant (f-EFT) \(\sigma \)-approximate single-source shortest-path tree (\(\sigma \)-ASPT), namely a subgraph of G having as few edges as possible and which, following the failure of a set F of at most f edges in G, contains paths from a fixed source that are stretched by a factor of at most \(\sigma \). To this respect, we provide an algorithm that efficiently computes an f-EFT \((2|F|+1)\)-ASPT of size O(fn). Our structure improves on a previous related construction designed for unweighted graphs, having the same size but guaranteeing a larger stretch factor of \(3(f+1)\), plus an additive term of \((f+1) \log n\). Then, we show how to convert our structure into an efficient f-EFT single-source distance oracle, that can be built in \(O(f m\, \alpha (m,n)+fn \log ^3 n)\) time, has size \(O(fn \log ^2 n)\), and in \(O(|F|^2 \log ^2 n)\) time is able to report a \((2|F|+1)\)-approximate distance from the source to any node in \(G-F\). Moreover, our oracle can return a corresponding approximate path in the same amount of time plus the path’s size. The oracle is obtained by tackling another fundamental problem, namely that of updating a minimum spanning forest (MSF) of G following a batch of k simultaneous modification (i.e., edge insertions, deletions and weight changes). For this problem, we build in \(O(m \log ^3 n)\) time an oracle of size \(O(m \log ^2 n)\), that reports in \(O(k^2 \log ^2 n)\) time the (at most 2k) edges either exiting from or entering into the MSF. Finally, for any integer \(k \ge 1\), we complement all our results with a lower bound of \(\Omega \left( n^{1+\frac{1}{k}}\right) \) to the size of any f-EFT \(\sigma \)-ASPT with \(f \ge \log n\) and \(\sigma < \frac{3k+1}{k+1}\), that holds if the Erdős’ girth conjecture is true.

Constant-time dynamic weight approximation for minimum spanning forest

We give two fully dynamic algorithms that maintain a (1+ε)-approximation of the weight M of a minimum spanning forest (MSF) of an n-node graph G with edges weights in [1,W], for any ε>0. (1) Our deterministic algorithm takes O(W2log⁡W/ε3)worst-case update time, which is O(1) if both W and ε are constants. (2) Our randomized (Monte-Carlo style) algorithm works with high probability and runs in worst-caseO(log⁡W/ε4) update time if W=O((m⁎)1/6/log2/3⁡n), where m⁎ is the minimum number of edges in the graph throughout all the updates. It works even against an adaptive adversary.We complement our algorithmic results with two cell-probe lower bounds for dynamically maintaining an approximation of the weight of an MSF of a graph.

Centralized Multi-Hop Routing Based on Multi-Start Minimum Spanning Forest Algorithm in the Wireless Sensor Networks.

Wireless sensor networks (WSNs) are widely applied in environmental monitoring, target tracking, military, and industrial fields. However, the battery energy of sensor nodes in WSNs is limited, which limits its development. Previous studies have shown that clustering protocols and multi-hop communication are beneficial to reduce nodes energy consumption. The multi-hop protocol based on low energy adaptive clustering hierarchy (LEACH) has been proven to significantly reduce energy dissipation. However, LEACH-based multi-hop protocols generally have the problem of unbalanced energy dissipation and data conflicts. In this paper, we propose a centralized multi-hop routing based on multi-start minimum spanning forest (LEACH-CMF) to optimize LEACH. In order to realize multi-hop communication, we introduced a multi-start minimum spanning tree algorithm to select relay nodes with the minimum relay cost and generate appropriate multi-hop paths. To avoid data collision in multi-hop communication and make nodes including the cluster heads sleep as much as possible in the non-working state, we design a bottom-up continuous time slot allocation method to improve the time division multiple access (TDMA) cycle. We performed simulation in NS2. The simulation results show that the network lifetime is approximately doubled compared to LEACH and centralized low energy adaptive clustering hierarchy (LEACH-C). The simulation results show that the proposed protocol can effectively balance the energy dissipation of nodes and prolong network lifetime.

Spectral-Spatial Hyperspectral Image Classification Based on Homogeneous Minimum Spanning Forest

The combination of spectral and spatial information is known as a suitable way to improve the accuracy of hyperspectral image classification. In this paper, we propose a spectral-spatial hyperspectral image classification approach composed of the following stages. Initially, the support vector machine (SVM) is applied to obtain the initial classification map. Then, we present a new index called the homogeneity order and, using that with K-nearest neighbors, we select some pixels in feature space. The extracted pixels are considered as markers for Minimum Spanning Forest (MSF) construction. The class assignment to the markers is done using the initial classification map results. In the final stage, MSF is applied to these markers, and a spectral-spatial classification map is obtained. Experiments performed on several real hyperspectral images demonstrate that the classification accuracies obtained by the proposed scheme are improved when compared to MSF-based spectral-spatial classification approaches.

Parallel graph algorithms in constant adaptive rounds

We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we focus on the Adaptive Massively Parallel Computation (AMPC) model, which is a theoretical model that captures MapReduce-like computation augmented with a distributed hash table. We show the first AMPC algorithms for all of the studied problems that run in a constant number of rounds and use only O ( n ϵ ) space per machine, where 0 &lt; ϵ &lt; 1. Our results improve both upon the previous results in the AMPC model, as well as the best-known results in the MPC model, which is the theoretical model underpinning many popular distributed computation frameworks, such as MapReduce, Hadoop, Beam, Pregel and Giraph. Finally, we provide an empirical comparison of the algorithms in the MPC and AMPC models in a fault-tolerant distributed computation environment. We empirically evaluate our algorithms on a set of large real-world graphs and show that our AMPC algorithms can achieve improvements in both running time and round-complexity over optimized MPC baselines.

A novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification

The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized spatial information. To this end, in this paper, novel gradient-based marker selection technique, dissimilarity measures, and adaptive connection weighting method are proposed by making full use of spatial information in hyperspectral image. Concretely, for a given hyperspectral image, a pixel-wise classification is firstly performed, and meanwhile the gradient map is generated by a morphology-based algorithm. Secondly, the most reliable pixels are selected as the markers from the classification map, and then the boundary noise pixels are excluded from the marker map by using the gradient map. Thirdly, several new dissimilarity measures are proposed by incorporating gradient information or probability information of pixels. Furthermore, in the growth procedure of MSF, the connection weighting between pixels is adjusted adaptively to improve the robustness of the MSF algorithm. Finally, when building the final classification map by using the majority voting rule, the labels of the training samples are used to dominate the label prediction. Experimental results are performed on two hyperspectral image sets Indian Pines and University of Pavia with different resolutions and contexts. The proposed approach yields higher classification accuracies compared to previously proposed classification methods, and provides accurate segmentation maps.

Комбінований метод нечіткої кластеризації даних в системах технічної діагностики

У статті наведено результати аналізу методів нечіткої кластеризації даних поточних спостережень в технічних системах. Запропоновано і протестовано комбінований підхід до нечіткої кластеризації даних з використанням комбінації алгоритму Густафсона-Кесселя, методу остовного лісу та фільтра Калмана, що може бути використаний в системах технічної діагностики (зокрема, діагностики стану основних параметрів технологічних процесів). Розглянуто можливість подальшого розширення функціональних можливостей наведеного комбінованого методу кластеризації з наданням йому адаптивних властивостей. Наведено результати тестування та визначено переваги запропонованого підходу.

Improving Spatial-Spectral Classification of Hyperspectral Imagery by Using Extended Minimum Spanning Forest Algorithm

Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. Recently, an effective approach for spatial-spectral classification has been proposed using Minimum Spanning Forest (MSF) algorithm. Our goal is to improve this approach to the classification of hyperspectral images in urban areas. In the proposed method two spatial/texture features, using wavelet and Gabor filters, are first extracted. The Weighted Genetic (WG) algorithm is then used to obtain the subspace of hyperspectral data and texture features. They are then fed into a novel marker-based MSF classification algorithm. In this algorithm, the markers are extracted from the two spatial-spectral classification maps. To evaluate the efficiency of the proposed approach two image datasets, Pavia University acquired by ROSIS-03 and Berlin by HyMap, were used. Experimental results demonstrate that the proposed approach achieves approximately 17% and 14% better overall accuracy than the original MSF-based algorithm for these datasets, respectively.

Solution of minimum spanning forest problems with reliability constraints

We propose the reliability constrained k-rooted minimum spanning forest, a relevant optimization problem whose aim is to find a k-rooted minimum cost forest that connects given customers to a number of supply vertices, in such a way that a minimum required reliability on each path between a customer and a supply vertex is satisfied and the cost is a minimum. The reliability of an edge is the probability that no failure occurs on that edge, whereas the reliability of a path is the product of the reliabilities of the edges in such path. The problem has relevant applications in the design of networks, in fields such as telecommunications, electricity and transports. For its solution, we propose a mixed integer linear programming model, and an adaptive large neighborhood search metaheuristic which invokes several shaking and local search operators. Extensive computational tests prove that the metaheuristic can provide good quality solutions in very short computing times.

GPU implementation of Borůvka’s algorithm to Euclidean minimum spanning tree based on Elias method

We present both sequential and data parallel approaches to build hierarchical minimum spanning forest (MSF) or tree (MST) in Euclidean space (EMSF/EMST) for applications whose input N points are uniformly or boundedly distributed in Euclidean space. Each iteration of the sequential approach takes O(N) time complexity through combining Borůvka’s algorithm with an improved component-based neighborhood search algorithm, namely sliced spiral search, which is a newly proposed improvement to Bentley’s spiral search for finding a component graph’s closest outgoing point on the plane. It works based on the uniqueness property in Euclidean space, and allows O(1) time complexity for one search from a query point to find the component’s closest outgoing point at different iterations of Borůvka’s algorithm. The data parallel approach includes a newly proposed two-direction breadth-first search (BFS) implementation on graphics processing unit (GPU) platform, which is specialized for selecting a spanning tree’s shortest outgoing edge. This GPU two-direction parallel BFS enables a tree traversal operation to start from any one of its vertex acting as root. These GPU parallel implementations work by assigning N threads with one thread associated to one input point, one thread occupies O(1) local memory and the whole algorithm occupies O(N) global memory. Experiments are conducted on both uniformly distributed data sets and TSPLIB database. We evaluate computation time of the proposed approaches on more than 80 benchmarks with size N growing up to 106 points on personal laptop.

AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA

Abstract. In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

A multiscale modified minimum spanning forest method for spatial-spectral hyperspectral images classification

This paper aimed to present a new method for the spectral-spatial classification of hyperspectral images, based on the idea of modified minimum spanning forest (MMSF). MMSF works on the obtained regions of pre-segmentation step that are considered as nodes of an image graph. In the proposed method, the image is first smoothed by the multiscale edge-preserving filter (MSEPF) and then the MMSF is built in each scale. Finally, all the classification maps of each scale are combined with a majority vote rule. The suggested method, named as MSEPF-MMSF, is performed on four hyperspectral images with different properties, and the experiments deal with the impacts of parameters of filter and the number of markers. The results demonstrate that the proposed method has improved the classification accuracies with respect to the previous methods.

A Hybrid Multi-scale Spatial Filtering and Minimum Spanning Forest for Spectral–Spatial Hyperspectral Image Classification

Integration of spatial and spectral information is an effective way in improving classification accuracy. In this article a new framework, based on multi-scale spatial weighted mean filtering (MSWMF) and minimum spanning forest, is proposed for the spectral–spatial classification of hyperspectral images. In the proposed framework, at first the image is smoothed by MSWMF and then the first eight principal components are extracted. Using support vector machine, at each scale of MSWMF, a classification map is produced in order to generate a marker map in the next step. Then, the minimum spanning forest is built on the marker map. Finally, in order to create a final classification map, all the classification maps of each scale are merged with a majority vote rule. The experimental results of the hyper-spectral images indicate that the suggested framework enhances the classification accuracy, in comparison with previously classification techniques. So, it is interesting for hyperspectral images classification.

Improving Spectral–spatial Classification of Hyperspectral Imagery Using Spectral Dimensionality Reduction Based on Weighted Genetic Algorithm

This paper aims to improve the accuracy and the efficiency of high resolution land cover mapping in urban area. To this end, an improved approach for classification of hyperspectral imagery is proposed and evaluated. This approach benefits from both inherent spectral and spatial information of an image. The weighted genetic (WG) algorithm is first used to obtain the subspace of hyperspectral data. The obtained features are then fed into the enhanced marker-based minimum spanning forest (EMSF) classification algorithm. In this algorithm, the markers are extracted from the classification maps obtained by both support vector machine and watershed segmentation algorithm classifiers. For this purpose, the class’s pixels with the largest population in the classification map are kept for each region of the segmentation map. Then, the most reliable classified pixels are chosen from among the exiting pixels as markers. To evaluate the efficiency of the proposed approach, three hyperspectral data sets acquired by ROSIS-03, Hymap and Hyper-Cam LWIR are used. Experimental results showed that the proposed WG–EMSF approach achieves approximately 9, 8 and 6% better overall accuracy than the original MSF-based algorithm for these data sets respectively.

Mapping urban land cover based on spatial-spectral classification of hyperspectral remote-sensing data

ABSTRACTIn this article, an innovative classification framework for hyperspectral image data, based on both spectral and spatial information, is proposed. The main objective of this method is to improve the accuracy and efficiency of high-resolution land-cover mapping in urban areas. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MMSF) algorithm. A pixel-based support vector machine (SVM) algorithm is first used to classify the hyperspectral image data, then the enhanced MMSF algorithm is applied in order to increase the accuracy of less accurately classified land-cover types. The enhanced MMSF algorithm is used as a binary classifier. These two classes are the low-accuracy class and remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. In the proposed approach, namely MSF-SVM, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithms, and are then used to build the MSF. Three benchmark hyperspectral data sets are used for the assessment: Berlin, Washington DC Mall, and Quebec City. Experimental results demonstrate the superiority of the proposed approach compared with SVM and the original MMSF algorithms. It achieves approximately 5, 6, and 7% higher rates in kappa coefficients of agreement in comparison with the original MMSF algorithm for the Berlin, Washington DC Mall, and Quebec City data sets, respectively.

A COMPARISON STUDY OF DIFFERENT MARKER SELECTION METHODS FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES

Abstract. An effective approach based on the Minimum Spanning Forest (MSF), grown from automatically selected markers using Support Vector Machines (SVM), has been proposed for spectral-spatial classification of hyperspectral images by Tarabalka et al. This paper aims at improving this approach by using image segmentation to integrate the spatial information into marker selection process. In this study, the markers are extracted from the classification maps, obtained by both SVM and segmentation algorithms, and then are used to build the MSF. The segmentation algorithms are the watershed, expectation maximization (EM) and hierarchical clustering. These algorithms are used in parallel and independently to segment the image. Moreover, the pixels of each class, with the largest population in the classification map, are kept for each region of the segmentation map. Lastly, the most reliable classified pixels are chosen from among the exiting pixels as markers. Two benchmark urban hyperspectral datasets are used for evaluation: Washington DC Mall and Berlin. The results of our experiments indicate that, compared to the original MSF approach, the marker selection using segmentation algorithms leads in more accurate classification maps.