Sampling Fingerprints From Multimedia Content Resource Clusters

Nowadays, the growth of multimedia content over the web is exponential. The fingerprints are inconspicuously embedded in multimedia content. The fingerprints can be exploited to trace divergent information from multimedia resources. Sampling fingerprints, particularly from multimedia resources, is challenging since they are complex, heterogeneous, and diverse. This research proposed an approach to sample fingerprints from multimedia resources. Our approach partitions the multimedia content space into converged clusters using variations of Canberra distance and identifies the most diverged samples using Kullback-Leibler (KL) divergence. The resultant clusters represent the information belonging to particular concepts and the diverged samples within the clusters represent multimedia fingerprints. The fingerprint sampling process is leveraged using unsupervised learning algorithms, instantiated across various multimedia descriptors, and tested over standard multimedia datasets. The average results obtained over various standard visual and acoustic datasets reveal 80%, 77%, and 78% accuracy, precision, and recall, respectively, surpassing most of the existing baseline clustering methods such as K-Means, Mean-Shift, and DBSCAN. Furthermore, the rigorousness of the proposed algorithm clustering is evaluated using the internal clustering stability silhouette coefficient and the fingerprint diversity scores. The results unveil a maximum of 94% diversity score. The proposed variation of Canberra distance and KL divergence provides the most stable performance (SD=0.02) and creates promising implications in future multimedia retrieval, summarization, and exploration activities.