2,584 research outputs found
Color Image Clustering using Block Truncation Algorithm
With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters
Brain image clustering by wavelet energy and CBSSO optimization algorithm
Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights.
The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes
Semantic-Enhanced Image Clustering
Image clustering is an important and open-challenging task in computer
vision. Although many methods have been proposed to solve the image clustering
task, they only explore images and uncover clusters according to the image
features, thus being unable to distinguish visually similar but semantically
different images. In this paper, we propose to investigate the task of image
clustering with the help of a visual-language pre-training model. Different
from the zero-shot setting, in which the class names are known, we only know
the number of clusters in this setting. Therefore, how to map images to a
proper semantic space and how to cluster images from both image and semantic
spaces are two key problems. To solve the above problems, we propose a novel
image clustering method guided by the visual-language pre-training model CLIP,
named \textbf{Semantic-Enhanced Image Clustering (SIC)}. In this new method, we
propose a method to map the given images to a proper semantic space first and
efficient methods to generate pseudo-labels according to the relationships
between images and semantics. Finally, we propose performing clustering with
consistency learning in both image space and semantic space, in a
self-supervised learning fashion. The theoretical result of convergence
analysis shows that our proposed method can converge at a sublinear speed.
Theoretical analysis of expectation risk also shows that we can reduce the
expected risk by improving neighborhood consistency, increasing prediction
confidence, or reducing neighborhood imbalance. Experimental results on five
benchmark datasets clearly show the superiority of our new method
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