Optimum-Path Forest based on k-connectivity: Theory and applications
نویسندگان
چکیده
منابع مشابه
Supervised pattern classification based on optimum-path forest
We present an approach for supervised classification, which interprets a training set as a complete graph, identifies prototypes in all classes, and computes an optimum-path forest rooted at them. The class of a sample in a tree is assumed to be the same of its root. A test sample is classified by identifying which tree would contain it. We show how to improve performance from the errors on an ...
متن کاملECG arrhythmia classification based on optimum-path forest
An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the noninvasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a f...
متن کاملOptimum Path Forest Approach for Image Retrieval based on Context
CBIR System consist of large datasets with millions of image samples for statistical analysis, hence putting tremendous challenge for pattern recognition techniques, which needs to be more efficient without compromising effectiveness. The image samples are stored in a database in the form of feature vectors. Pattern Recognition Technique requires a high computational burden for learning the dis...
متن کاملA comparison between k-Optimum Path Forest and k-Nearest Neighbors supervised classifiers
This paper presents the k-Optimum Path Forest (k-OPF) supervised classifier, which is a natural extension of the OPF classifier. k-OPF is compared to the k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Decision Tree (DT) classifiers, and we see that k-OPF and k-NN have many similarities. This work shows that the k-OPF is equivalent to the k-NN classifier when all training samples a...
متن کاملSupervised Pattern Classification Using Optimum-Path Forest
We present a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), and describe one of its classifiers developed for the supervised learning case. This classifier does not require parameters and can handle some overlapping among multiple classes with arbitrary shapes. The method reduces the pattern recognition problem into the computation of an optimum-path forest in ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2017
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2016.07.026