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This paper presents novel techniques for image retrieval using the clustering features extracted The proposed techniques are compared with well known traditional technique such as Hierarchical Clustering. Hierarchical clustering starts by calculating the Euclidean distance measure for all patterns in data set, which is not required to calculate in proposed(More)
A technique to retrieve images by region matching using a combined feature index based on color, shape, and location is presented within the framework of MPEG-7. Dominant regions within each image are indexed using integrated color, shape, and location features. Various combinations of regions are also indexed. The resulting indices and related metadata are(More)
Our world is dominated by visual information and a tremendous amount of such information is being added day-by-day. It would be impossible to cope with this explosion of visual data, unless they are organized such that we can retrieve them efficiently and effectively. The main problem in organizing and managing such visual data is indexing, the assignment(More)
Most CBIR systems use low-level visual features for representation and retrieval of images. Generally such methods suffer from the problems of high-dimensionality leading to more computational time and inefficient indexing and retrieval performance. This paper focuses on a low-dimensional color and shape based indexing technique for achieving efficient and(More)
In this paper we propose an evolutionary learning based fuzzy theoretic approach for classifying video sequences into generic categories. This categorization is based on video structure based syntactic features. The features like shot durations, editing style, camera work and shot activity conveys large amount of information about the type of video. The(More)
— Recently there has been considerable interest in applying evolutionary and natural computing techniques for analysing large datasets with large number of features. In particular, efficacy prediction of siRNA has attracted a lot of researchers, because of large number of features involved. In the present work, we have applied the SVM based classifier along(More)