Selective Image Segmentation Models Using Three Distance Functions

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models.

Selective Image Segmentation Models Using Three Distance Functions

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models.

Improving Euclidean’s Consensus Degrees in Group Decision Making Problems Through a Uniform Extension

In a Group Decision Making problem, several people try to reach a single common decision by selecting one of the possible alternatives according to their respective preferences. So, a consensus process is performed in order to increase the level of accord amongst people, called experts, before obtaining the final solution. Improving the consensus degree as much as possible is a very interesting task in the process. In the evaluation of the consensus degree, the measurement of the distance representing disagreement among the experts’ preferences should be considered. Different distance functions have been proposed to implement in consensus models. The Euclidean distance function is one of the most commonly used. This paper analyzes how to improve the consensus degrees, obtained through the Euclidean distance function, when the preferences of the experts are slightly modified by using one of the properties of the Uniform distribution. We fulfil an experimental study that shows the betterment in the consensus degrees when the Uniform extension is applied, taking into account different number of experts and alternatives.

Trajectory Clustering within the Terminal Airspace Utilizing a Weighted Distance Function

To support efforts to modernize aviation systems to be safer and more efficient, high-precision trajectory prediction and robust anomaly detection methods are required. The terminal airspace is identified as the most critical airspace for individual flight-level and system-level safety and efficiency. To support successful trajectory prediction and anomaly detection methods within the terminal airspace, accurate identification of air traffic flows is paramount. Typically, air traffic flows are identified utilizing clustering algorithms, where performance relies on the definition of an appropriate distance function. The convergent/divergent nature of flows within the terminal airspace makes the definition of an appropriate distance function challenging. Utilization of the Euclidean distance is standard in aviation literature due to little computational expense and ability to cluster entire trajectories or trajectory segments at once. However, a primary limitation in the utilization of the Euclidean distance is the uneven distribution of distances as aircraft arrive at or depart from the airport, which may result in skewed classification and inadequate identification of air traffic flows. Therefore, a weighted Euclidean distance function is proposed to improve trajectory clustering within the terminal airspace. In this work, various weighting schemes are evaluated, applying the HDBSCAN algorithm to cluster the trajectories. This work demonstrates the promise of utilizing a weighted Euclidean distance function to improve the identification of terminal airspace air traffic flows. In particular, for the selected terminal airspace, if trajectory points closer to the border of the terminal airspace, but not necessarily at the border, are weighted highest, then a more accurate clustering is computed.

Detection of Road Surface Changes from Multi-Temporal Unmanned Aerial Vehicle Images Using a Convolutional Siamese Network

Road quality commonly decreases due to aging and deterioration of road surfaces. As the number of roads that need to be surveyed increases, general maintenance—particularly surveillance—can be quite costly if carried out using traditional methods. Therefore, using unmanned aerial vehicles (UAVs) and deep learning to detect changes via surveys is a promising strategy. This study proposes a method for detecting changes on road surfaces using pairs of UAV images captured at different times. First, a convolutional Siamese network is introduced to extract the features of an image pair and a Euclidean distance function is applied to calculate the distance between two features. Then, a contrastive loss function is used to enlarge the distance between changed feature pairs and reduce the distance between unchanged feature pairs. Finally, the initial change map is improved based on the preliminary differences between the two input images. Our experimental results confirm the effectiveness of this approach.

Design and Development of Machine Learning Algorithm for Forecasting Crime Rate

Controlling crime is one of the necessary things for a peaceful life. Forecasting the crime helps in planning the strategies in this task. Modern data analysis techniques like classification and prediction may be utilized for this purpose. Classification is a data mining approach that allocates items in a group to target categories or classes. It also may be used to label a target item into any one of the classes identified.Among many available classification techniques, clustering is one of the unsupervised machine learning approaches that could be used for creating clusters as features to enhance classification models. There are various clustering algorithm available like K-mean clustering, Kernel K-mean algorithm etc.PCA algorithm is used to reduce the dimension of the huge amount of data used so that the data can be represented in smaller database with reduced noise in the dataset. In general, mode is a set of values which occurs frequently. Hence, instead of k-mean which is an average value, frequent values may produce better result.K-Mean algorithm creates clusters and groups data properly. But randomly assuming centroid for clusters in the initial stage leads to too much of computational cost. So, in this work, K mode Clustering algorithm was used for clustering asit replaces the Euclidean distance function with the simple matching dissimilarity measure. Once the clusters were formed, a new algorithm was used to forecast the crime rate or future values of the data in the cluster.The proposed approach was tested on crime dataset and found efficient in this domain while comparing with some existing approaches

Reigniting GIS's Application in Ecotourism

The chapter brings out a brief note on the tourist attractions, hotels and lodges, NGOs/travel agencies operating in that region, railway/bus stations, land use profile, etc. in the Sundarban area of West Bengal in conjunction with exploring the potential of ecotourism using GIS and some secondary source data. Moving onto the analysis part, by making use of geo-spatial data, the attributes of ecotourism potential in the Sundarbans has been explored. The author makes use of the Euclidean distance mechanism and principal component analysis to rank the ecotourism sites in Sunderbans (i.e., based on the construction of ecotourism potential index [EPI]). The novelty of the chapter lies in comparing the ranks obtained by constructing the EPI following the principal component analysis and the Euclidean distance function. It needs to be mentioned here that these tourist spots have been selected based on the information collected on the inflow of both domestic and foreign tourists to these spots. The chapter concludes by discussing the future scope of research in this regard.

Reigniting GIS's Application in Ecotourism

The chapter brings out a brief note on the tourist attractions, hotels and lodges, NGOs/travel agencies operating in that region, railway/bus stations, land use profile, etc. in the Sundarban area of West Bengal in conjunction with exploring the potential of ecotourism using GIS and some secondary source data. Moving onto the analysis part, by making use of geo-spatial data, the attributes of ecotourism potential in the Sundarbans has been explored. The author makes use of the Euclidean distance mechanism and principal component analysis to rank the ecotourism sites in Sunderbans (i.e., based on the construction of ecotourism potential index [EPI]). The novelty of the chapter lies in comparing the ranks obtained by constructing the EPI following the principal component analysis and the Euclidean distance function. It needs to be mentioned here that these tourist spots have been selected based on the information collected on the inflow of both domestic and foreign tourists to these spots. The chapter concludes by discussing the future scope of research in this regard.

Round Randomized Learning Vector Quantization for Brain Tumor Imaging

Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.