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Chromosome classification is the essential task of karyotyping analysis which is videly used for detecting genetic anomalities. Chromosomes' band profiles based feature vectors are used frequently in the classification of them. Extracting of band profiles specially in the bent parts of chromosome may be lossy. In this study, an special sub-pixel resolution based method for lossless exctraction of...
Spectral clustering is able to extract clusters with various characteristics without a parametric model, however it is infeasible for large datasets due to its high computational cost and memory requirement. Approximate spectral clustering (ASC) addresses this challenge by a representative-based partitioning approach which first finds a set of data representatives either by sampling or quantization,...
Approximate spectral clustering (ASC), a recently popular approach for unsupervised land cover identification, applies spectral clustering on a reduced set of data representatives (found by sampling or quantization). ASC enables extraction of clusters with different characteristics by utilizing various information types (such as distance, local density distribution and data topology) for accurate...
Unsupervised clustering of high spatial resolution remote-sensing images plays a significant role in detailed land-cover identification, especially for agricultural and environmental monitoring. A recently promising method is approximate spectral clustering (SC) which enables spectral partitioning for large datasets to extract clusters with distinct characteristics without a parametric model. It also...
Considering the economic and environmental aspects, hazelnut orchards are of great importance in Turkey. It is crucial to develop methods for detecting, monitoring, protecting and managing these orchards. This can be done exactly and fast by evaluating the remote sensing images of these areas. For this aim clustering methods are so popular due to their unsupervised nature. Particularly, spectral clustering...
Spectral clustering has been successfully used in various applications, thanks to its properties such as no requirement of a parametric model, ability to extract clusters of different characteristics and easy implementation. However, it is often infeasible for large datasets due to its heavy computational load and memory requirement. To utilize its advantages for large datasets, it is applied to the...
Spectral clustering has been successfully used in many applications thanks to its ability to extract clusters with various characteristics without a parametric model and its easy implementation. However, due to its computational cost and memory requirement, it is infeasible for big data such as remote sensing images and it can only be applied through data representatives (obtained by quantization)...
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