Abstract
Due to poor and non-uniform lighting conditions of the object, imprecise boundaries and color values, the use of fuzzy systems makes a viable addition in image analysis. Given a continuous video sequence V, the first step in this framework for mining video data is to parse it into discrete frames. This is an important task since it preserves the temporal information associated with every frame. A database of images is created, from which features are extracted for each image and stored in feature database. This framework focuses on color as feature and considers HLS color space with color quantization into eight colors. Using fuzzy rules, fuzzy histogram of all these eight colors is calculated and stored in feature database. A Radial Basis Function (RBF) Neural Network is trained by the fuzzy histogram of random images and similarity measure is calculated with all other frames. Frames, which have distance between ranges specified for clustering, are clustered into one cluster.
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© 2004 Springer-Verlag Berlin Heidelberg
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Manori A., M., Maheshwari, M., Belawat, K., Jain, S., Chande, P.K. (2004). Neuro-fuzzy System for Clustering of Video Database. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_149
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DOI: https://doi.org/10.1007/978-3-540-30499-9_149
Publisher Name: Springer, Berlin, Heidelberg
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