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Mean shift, mode seeking, and clustering
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... AbstractMean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a modeseeki ..."
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Cited by 620 (0 self)
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in clustering and Hough transform are demonstrated. Mean shift is also considered as an evolutionary strategy that performs multistart global optimization. Index TermsMean shift, gradient descent, global optimization, Hough transform, cluster analysis, kmeans clustering. I.
Mean shift is a bound optimization
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Abstract—We build on the current understanding of mean shift as an optimization procedure. We demonstrate that, in the case of piecewise constant kernels, mean shift is equivalent to Newton’s method. Further, we prove that, for all kernels, the mean shift procedure is a quadratic bound maximization. ..."
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Cited by 48 (0 self)
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. Index Terms—Mean shift, bound optimization, Newton’s method, adaptive gradient descent, mode seeking.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.??, NO.??, MONTH YEAR 1 Mean Shift is a Bound Optimization
, 2004
"... We build on the current understanding of mean shift as an optimization procedure. We demonstrate that in the case of piecewise constant kernels mean shift is equivalent to Newton’s method. Further, we prove that for all kernels the mean shift procedure is a quadratic bound maximization. INDEX TERMS ..."
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We build on the current understanding of mean shift as an optimization procedure. We demonstrate that in the case of piecewise constant kernels mean shift is equivalent to Newton’s method. Further, we prove that for all kernels the mean shift procedure is a quadratic bound maximization. INDEX TERMS
Probabilistic Latent Semantic Indexing
, 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
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Cited by 1207 (11 self)
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Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized
Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding
 IEEE TRANS. ON INFORMATION THEORY
, 1999
"... We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing informationembedding rate, mini ..."
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Cited by 495 (15 self)
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, minimizing distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortioncompensated QIM (DCQIM), and develop convenient realizations in the form of what we
Managing Gigabytes: Compressing and Indexing Documents and Images  Errata
, 1996
"... > ! "GZip" page 64, Table 2.5, line "progp": "43,379" ! "49,379" page 68, Table 2.6: "Mbyte/sec" ! "Mbyte/min" twice in the body of the table, and in the caption "Mbyte/second" ! "Mbyte/minute" page 70, para 4, line ..."
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Cited by 985 (48 self)
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> ! "GZip" page 64, Table 2.5, line "progp": "43,379" ! "49,379" page 68, Table 2.6: "Mbyte/sec" ! "Mbyte/min" twice in the body of the table, and in the caption "Mbyte/second" ! "Mbyte/minute" page 70, para 4, line 5: "Santos" ! "Santis" page 71, line 11: "Fiala and Greene (1989)" ! "Fiala and Green (1989)" Chapter Three page 89, para starting "Using this method", line 2: "hapax legomena " ! "hapax legomenon " page 96, line 5: "a such a" ! "such a" page 98, line 6: "shows that in fact none is an answer to this query" ! "shows that only document 2 is an answer to this query" page 106, para 3, line 9: "the bitstring in Figure 3.7b" ! "the bitstring in Figure 3.7c" page 107, Figure 3.7: The coding shown in part (c) cannot be decoded ambiguously. For example, the sequence "1010 0000 0001 0000
FastMap: A Fast Algorithm for Indexing, DataMining and Visualization of Traditional and Multimedia Datasets
, 1995
"... A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in kd space, using k featureextraction functions, provided by a domain expert [25]. Thus, we can subsequently use highly finetuned spatial access methods (SAMs), to answer several types ..."
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Cited by 497 (23 self)
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A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in kd space, using k featureextraction functions, provided by a domain expert [25]. Thus, we can subsequently use highly finetuned spatial access methods (SAMs), to answer several types of queries, including the `Query By Example' type (which translates to a range query); the `all pairs' query (which translates to a spatial join [8]); the nearestneighbor or bestmatch query, etc. However, designing feature extraction functions can be hard. It is relatively easier for a domain expert to assess the similarity/distance of two objects. Given only the distance information though, it is not obvious how to map objects into points. This is exactly the topic of this paper. We describe a fast algorithm to map objects into points in some kdimensional space (k is userdefined), such that the dissimilarities are preserved. There are two benefits from this mapping: (a) efficient ret...
A theory of fairness, competition and cooperation
 Quarterly Journal of Economics
, 1999
"... de/ls_schmidt/index.html ..."
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