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Recognition and mining (RM) applications are an emerging class of computing workloads that will be commonly executed on future multi-core and many-core computing platforms. The explosive growth of input data and the use of more sophisticated algorithms in RM applications will ensure, for the foreseeable future, a significant gap between the computational needs of RM applications and the capabilities...
Many of today's real world applications need to handle and analyze continually growing amounts of data, while the cost of collecting data decreases. As a result, the main technological hurdle is that the data is acquired faster than it can be processed. Data reduction methods are thus increasingly important, as they allow one to extract the most relevant and important information from giant data sets...
The CLARA algorithm is one of the popular clustering algorithms in use nowadays. This algorithm works on a randomly selected subset of the original data and produces near accurate results at a faster rate than other clustering algorithms. CLARA is basically used in data mining applications. We have used this algorithm for color image segmentation.The original CLARA is modified for producing better...
The paper presents a new approach for feature representation using semantic line groupings in an image. The algorithm uses the hypothesis in line with Gestalt laws of proximity that as a baseline in an image, semantic structures are formed by line segments placed in close proximity to each other. The algorithm uses line segments in an image to form semantic groups based on a minimum distance threshold...
Matching video segments in order to detect their similarity is a necessary task in retrieval and summarization applications. In order to determine nearly identical content, such as repeated takes of the same scene, very precise matching of sequences of features extracted from the video segments needs to be performed. In this paper we compare the performance of three distance measures for the task...
In machine learning or data mining research area, clustering is definitely an active topic and has drawn a lot of attention for its significance in practical applications, such as image segmentation, data analysis, text mining and so on. There have been a great number of clustering algorithms derived from different points of view. K-means is widely known as a straightforward and fairly efficient method...
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