Abstract
Two important approaches in multimedia information retrieval are classification and the ranking of the retrieved results. The technique of performing classification using Association Rule Mining (ARM) has been utilized to detect the high-level features from the video, taking advantages of its high efficiency and accuracy. Motivated by the fact that the users are only interested in the top-ranked relevant results, ranking strategies have been adopted to sort the retrieved results. In this paper, an effective and efficient video high-level semantic retrieval framework that utilizes associations and correlations to retrieve and rank the high-level features is developed. The n-feature-value pair rules are generated using a combined measure based on (1) the existence of the (n - 1)-feature-value pairs, where n is larger than 1, (2) the correlation between different n-feature-value pairs and the concept classes through Multiple Correspondence Analysis (MCA), and (3) the similarity representing the harmonic mean of the inter-similarity and intra-similarity. The final association classification rules are selected by using the calculated similarity values. Then our proposed ranking process uses the scores that integrate the correlation and similarity values to rank the retrieved results. To show the robustness of the proposed framework, experiments with 15 high-level features (concepts) and benchmark data sets from TRECVID and comparisons with 6 other well-known classifiers are presented. Our proposed framework achieves promising performance and outperforms all the other classifiers. Moreover, the final ranked retrieved results are evaluated by the mean average precision measure, which is commonly used for performance evaluation in the TRECVID community.
