Web Video Data Clustering and Recognition Using Histograms of Phoneme Symbols

The clustering and recognition of Web video content play an important role in multimedia information retrieval. This paper proposes a method for both clustering and recognizing Web video content using a histogram of phoneme symbols (HoPS). HoPS contains information about speech and sound intervals. In this study, three experiments were conducted.The first experiment allocated HoPS feature of video intervals in a 3D space using PCA and quantification method IV (Q-IV). The second experiment applied the k-nearest neighbor (k-NN) method to analyze the difficulties in clustering. The third experiment recognized unknown video intervals by using the distance between HoPS of the query and a category average. The accuracy of the recognition results were 44.3% and 36.9% using the Mahalanobis distance and the correlation distance for the category average of training data, respectively.