@article {15613,
	title = {An efficient k-means clustering algorithm: analysis and implementation},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {24},
	year = {2002},
	month = {2002/07//},
	pages = {881 - 892},
	abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R<sup>d</sup> and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd{\textquoteright}s (1982) algorithm. We present a simple and efficient implementation of Lloyd{\textquoteright}s k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm{\textquoteright}s running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation},
	keywords = {algorithm;color, algorithm;image, algorithm;kd-tree;mean, analysis;filtering, clustering, clustering;, compression;data, distance;covariance, Lloyd, matrices;filtering, quantization;data, segmentation;k-means, squared, structure;data-sensitive, theory;pattern},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2002.1017616},
	author = {Kanungo,T. and Mount, Dave and Netanyahu,N. S and Piatko,C. D and Silverman,R. and Wu,A. Y}
}
@conference {18162,
	title = {An algorithm for wipe detection},
	booktitle = {Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on},
	volume = {1},
	year = {1998},
	month = {1998/10//},
	pages = {893 -897 vol.1 - 893 -897 vol.1},
	abstract = {The detection of transitions between shots in video programs is an important first step in analyzing video content. The wipe is a frequently used transitional form between shots. Wipe detection is more involved than the detection of abrupt and other gradual transitions because a wipe may take various patterns and because of the difficulty in discriminating a wipe from object and camera motion. In this paper, we propose an algorithm for detecting wipes using both structural and statistical information. The algorithm can effectively detect most wipes used in current TV programs. It uses the DC sequence which can be easily extracted from the MPEG stream without full decompression},
	keywords = {algorithm;image, analysis;video, content;video, DC, DETECTION, detection;statistical, domain;object, information;structural, information;video, motion;compressed, motion;shot, processing;, programs;camera, programs;wipe, sequence;MPEG, sequences;signal, signal, stream;TV, transitions},
	doi = {10.1109/ICIP.1998.723664},
	author = {M. Wu and Wolf,W. and Liu,B.}
}
