Optimization of unsupervised affinity propagation clustering method

In this paper, we propose a true unsupervised method to partition large-size images, where the number of classes, training samples, and other a priori information is not known. Thus, partitioning an image without any knowledge is a great challenge. This novel adaptive and hierarchical classification method is based on affinity propagation, where all criteria and parameters are adaptively calculated from the image to be partitioned. It is reliable to objectively discover classes of an image without user intervention and therefore satisfies all the objectives of an unsupervised method. Hierarchical partitioning adopted allows the user to analyze and interpret the data very finely. The optimal partition maximizing an objective criterion provides the number of classes and the exemplar of each class. The efficiency of the proposed method is demonstrated through experimental results on hyperspectral images. The obtained results show its superiority over the most widely used unsupervised and semi-supervised methods. The developed method can be used in several application domains to partition large-size images or data. It allows the user to consider all or part of the obtained classes and gives the possibility to select the samples in an objective way during a learning process.

show abstract

“…This operation provides less biased estimates of R and A matrices, in contrast to those obtained in [42,63].…”

Section: Preference Parameter and Responsibility-availability Criteriamentioning

confidence: 99%

Hierarchical Unsupervised Partitioning of Large Size Data and Its Application to Hyperspectral Images

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Partitioning unsupervised methods, as defined in Refs. 24 and 25, can meet this need because they do not require the number of classes, associated learning samples (labeled data), or any other prior knowledge. The number of classes is objectively estimated according to a given optimization criterion or several optimization criteria.…”

Section: Introductionmentioning

confidence: 99%

Autonomous method for selection or validation of training samples for large size hyperspectral images

Alameddine

Chehdi

Cariou

2023

J. Appl. Rem. Sens.

Self Cite

View full text Add to dashboard Cite

Providing unbiased ground truths for large size images is a complex task that is difficult to achieve in practice. The aim of our method is to easily produce reliable ground truths, from images covering large areas while respecting the physical nature of the observed data. The first step localizes all classes existing in a large size image without any a prior knowledge, as well as the samples that make them up. Next, the user selects classes from these unbiased detected classes, in order to build a true ground truth adapted to this application. No transformation is carried out on of characteristics (spectral features) of the hyperspectral image pixels measured objectively by the sensor. To prove the relevance of the learning sample selection method, we use three supervised classification methods requiring training samples. Two hyperspectral images are selected to illustrate the performance of the obtained training samples. The results of the proposed method are also compared to those available of some state of the art deep learning methods.

show abstract

“…The joint spatial-spectral information is effectively incorporated into the proximity graph matrix of which the block diagonal structure is amplified by the "conductivity method" to improve the clustering performance [40]. The optimization of affinity propagation method is introduced to take into account the presence of identical objects in the samples to be partitioned and adapts the preference parameter to each object for HSI clustering [41]. Inspired by the success of MF in hyperspectral unmixing, a robust manifold method consisting of two MF components (RMMF) for HSI clustering is proposed in [42] and the clustering indicators can be directly obtained via the second MF component.…”

Section: Introductionmentioning

confidence: 99%

Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering

Qin

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

In this paper, we integrate the spatial-spectral information of HSI samples into non-negative matrix factorization (NMF) for affinity matrix learning to address the issue of HSI clustering. This technique consists of three main components: i) oversegmentation for computing the spectral-spatial affinity matrix, ii) NMF with the guidance of the obtained affinity matrix and iii) density-based spectral clustering on the final affinity matrix. First, the HSI is oversegmented into superpixels via the entropy rate superpixel (ERS) algorithm. The spectral-spatial affinity matrix is defined based on the class-consistency assumption of all the HSI samples in each superpixel and the similar HSI samples between adjacent superpixels. Second, to integrate the spectral-spatial information into NMF, the obtained affinity matrix is used to guide the iterative process of NMF. The spectralspatial affinity matrix is then weighted by the affinity matrix in the obtained low-dimensional subspace to form the final affinity matrix. Third, density-based spectral clustering is applied to the final affinity matrix to obtain clustering maps. Experimental results on three public benchmark HSIs demonstrate that the proposed method is superior to the considered state-of-the-art baseline methods on both the computational cost and clustering accuracy.

show abstract

Optimization of unsupervised affinity propagation clustering method

Cited by 3 publications

References 8 publications

Hierarchical Unsupervised Partitioning of Large Size Data and Its Application to Hyperspectral Images

Hierarchical Unsupervised Partitioning of Large Size Data and Its Application to Hyperspectral Images

Autonomous method for selection or validation of training samples for large size hyperspectral images

Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering

Contact Info

Product

Resources

About