Abstract Stable semantics is a prerequisite for achieving excellent image clustering. However, most current methods suffer from inaccurate class semantic estimation, which limits the clustering performance. For sake of addressing issue, we propose pseudo-supervised framework based on meta-features. First, mines meta-semantic features (i.e., meta-features) categories instance-level features, not...

Although there are many excellent clustering algorithms, effective clustering remains very challenging for large datasets that contain many classes. Image clustering presents further problems because automatically computed image distances are often noisy. We address these challenges in two ways. First, we propose a new algorithm to cluster a subset of the images only (we call this subclustering...

We present two new clustering algorithms for medical image segmentation based on the multimodal image registration and the information bottleneck method. In these algorithms, the histogram bins of two registered multimodal 3D-images are clustered by minimizing the loss of mutual information between them. Thus, the clustering of histogram bins is driven by the preservation of the shared informat...

Traditional image clustering systems are primarily based on visual and/or textual features. Such algorithms commonly suffer from the problem of semantic gap. General approach to overcome this problem is to incorporate user feedback. However, data extracted from certain domains like social networks, web-blogs etc. is evolving in nature. In other words, data collected from such domains over small...

For many of the state-of-the-art computer vision algorithms, image segmentation is an important preprocessing step. As such, several image segmentation algorithms have been proposed, however, with certain reservation due to high computational load and many hand-tuning parameters. Correlation clustering, a graphpartitioning algorithm often used in natural language processing and document cluster...