This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (ACCESS) derived from a similarity matrix. The ACCESS method uses an analysis based on the theoretical properties of spectral decomposition to identify data items that are likely to be located on the bo...

For many systems characterized as “complex” the patterns exhibited on different scales differ markedly from one another. For example the biomass distribution in a human body “looks very different” depending on the scale at which one examines it. Conversely, the patterns at different scales in “simple” systems (e.g., gases, mountains, crystals) vary little from one scale to another. Accordingly,...

Rice as one of the most important agricultural crops has a putative potential for ensuring food security and addressing poverty in the world. In the present study, in order to provide basic information to improve rice through breeding programs, Inter Simple Sequence Repeat marker (ISSR) was used For DNA fingerprinting and finding genetic relationships among 32 different cultivars. In this study...

Partitioning a data set and extracting hidden structure arises in diierent application areas of pattern recognition, data analysis and image processing. We formulate data clustering for data characterized by pairwise dissimilarity values as an assignment problem with an objective function to be minimized. An extension to tree{structured clustering is proposed which allows a hierarchical groupin...

Progresses made on content-based image retrieval has reactivated the research on image analysis and similarity-based approaches have been investigated to assess the similarity between images. In this paper, the content-based approach is extended towards the problem of image collection summarization and comparison. For these purposes we propose to carry out clustering analysis on visual features...

Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with ...