Semantic video indexing using context-dependent fusion

Dae-Jin Kim; Hichem Frigui; Aleksey Fadeev

doi:10.1117/12.766542

28 January 2008 Semantic video indexing using context-dependent fusion

Dae-Jin Kim, Hichem Frigui, Aleksey Fadeev

Proceedings Volume 6820, Multimedia Content Access: Algorithms and Systems II; 68200F (2008) https://doi.org/10.1117/12.766542
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

We present a novel method for fusing the results of multiple semantic video indexing algorithms that use different types of feature descriptors and different classification methods. This method, called Context-Dependent Fusion (CDF), is motivated by the fact that the relative performance of different semantic indexing methods can vary significantly depending on the video type, context information, and the high-level concept of the video segment to be labeled. The training part of CDF has two main components: context extraction and algorithm fusion. In context extraction, the low-level audio-visual descriptors used by the different classification algorithms are combined and used to partition the descriptors space into groups of similar video shots, or contexts. The algorithm fusion component identifies a subset of classification algorithms (local experts) for each context based on their relative performance within the context. Results on the TRECVID-2002 data collections show that the proposed method can identify meaningful and coherent clusters and that different labeling algorithms can be identified for the different contexts. Our initial experiments have indicated that the context-dependent fusion outperforms the individual algorithms. We also show that using simple visual descriptors and a simple K-NN classifier, the CDF approach provides results that are comparable to the state-of-the-art methods in semantic indexing.

Citation Download Citation

Dae-Jin Kim, Hichem Frigui, and Aleksey Fadeev "Semantic video indexing using context-dependent fusion", Proc. SPIE 6820, Multimedia Content Access: Algorithms and Systems II, 68200F (28 January 2008); https://doi.org/10.1117/12.766542

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KEYWORDS

Video

Semantic video

Detection and tracking algorithms

Classification systems

Feature extraction

Visualization

Image segmentation

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