Abstract
This paper presents a novel method for annotating videos taken from the TRECVID 2005 data using only static visual features and metadata of still image frames. The method is designed to provide the user with annotation or tagging tools to incorporate multimedia data such as video or still images as well as text into searching or other combined applications running either on the web or on other networks. It mainly uses MPEG-7-based visual features and metadata of prototype images and allows the user to select either a prototype or a training set. It also adaptively adjusts the weights of the visual features the user finds most adequate to bridge the semantic gap. The user can also detect relevant regions in video frames by using a self-developed segmentation tool and can carry out region-based annotation with the same video frame set. The method provides satisfactory results even when the annotations of the TRECVID 2005 video data greatly vary considering the semantic level of concepts. It is simple and fast, using a very small set of training data and little or no user intervention. It also has the advantage that it can be applied to any combination of visual and textual features.
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Kutics, A., Nakagawa, A., Shindoh, K. (2007). Use of Adaptive Still Image Descriptors for Annotation of Video Frames. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_61
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DOI: https://doi.org/10.1007/978-3-540-74260-9_61
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