Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Current widely employed clustering approaches may not yield satisfactory results with regard to the characteristics and distribution of datasets and number of clusters to be sought, especially for visual domains in multidimensional space. This study establishes a novel clustering methodology using a pairwise similarity matrix, Clustering Visual Objects in Pairwise Similarity Matrix (CVOIPSM), for grouping similar objects in specific visual domains. A dimensionality reduction and feature extraction technique, along with a distance measuring method and a newly established algorithm, Clustering in Pairwise Similarity Matrix (CIPSM), are combined to develop the CVOIPSM methodology. CIPSM utilizes both Rk-means and an agglomerative, contractible, expandable (ACE) technique to calculate a membership degree based on maximizing inter-class similarity and minimizing intra-class similarity. CVOIPSM has been tested on several datasets, with average success rates on downsized subsamples between 87.5\% and 97.75\% and between 81\% and 87\% on the larger datasets. The difference in the success rates for small and large datasets is not statistically significant (p>0.01). Moreover, this method automatically determines the likely number of clusters without any user dictation. The empirical results and the statistical significance test on these results ensure that CVOIPSM performs effectively and efficiently on specific visual domains, disclosing the interrelated patterns of similarities among objects

Context-driven Clustering by Multi-class Classification in an Active Learning Framework ∗

Tracking and detection of objects often require to apply complex models to cope with the large intra-class variability of the foreground as well as the background class. In this work, we reduce the complexity of a binary classification problem by a context-driven approach. The main idea is to use a hidden multi-class representation to capture multi-modalities in the data finally providing a binary classifier. We introduce virtual classes generated by a contextdriven clustering, which are updated using an active learning strategy. By further using an on-line learner the classifier can easily be adapted to changing environmental conditions. Moreover, by adding additional virtual classes more complex scenarios can be handled. We demonstrate the approach for tracking as well as detection on different scenarios reaching state-of-the-art results. 1