Searching for multimedia objects with heterogeneous modalities is critical for the construction of effective multimedia retrieval systems. Towards this direction, we propose a framework for the multimodal fusion of visual and textual similarities, based on visual features, visual concepts and textual concepts. Our method is compared to the baseline method that only fuses two modalities but integrates all early, late, linearly weighted, diffusion and graph-based models in one unifying framework. Our ...
Searching for multimedia objects with heterogeneous modalities is critical for the construction of effective multimedia retrieval systems. Towards this direction, we propose a framework for the multimodal fusion of visual and textual similarities, based on visual features, visual concepts and textual concepts. Our method is compared to the baseline method that only fuses two modalities but integrates all early, late, linearly weighted, diffusion and graph-based models in one unifying framework. Our framework integrates more than two modalities and high-level information, so as to retrieve multimedia objects enriched with high-level textual and visual concepts, in response to a multimodal query. The experimental comparison is done under the same memory complexity, in two multimedia collections in the multimedia retrieval task. The results have shown that we outperform the baseline method, in terms of Mean Average Precision.
+
