Title :
Successive approximation source coding and image enabled data mining
Author :
Barnes, Christopher F.
Author_Institution :
Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
This paper deals with successive approximation source coding and image enabled data mining. Successive approximation source codes provide query returns consisting of sequences of aggregate data-tuples with image sets. A data mining statistical analysis or pattern search over a sequence of aggregates provides a sequence of data mining answers that is desirably fuzzy. Residual vector quantization (RVQ) provides a successive approximation source code with utility in image-enabled queries in image data mining tasks.
Keywords :
data mining; image coding; image sequences; query processing; source coding; statistical analysis; vector quantisation; data-tuple sequence; image enabled data mining; image set; pattern search; query return; residual vector quantization; statistical analysis; successive approximation source coding; Aggregates; Algorithm design and analysis; Anthropometry; Cost accounting; Data mining; Data warehouses; Humans; Information analysis; Source coding; Vector quantization;
Conference_Titel :
Data Compression Conference, 2004. Proceedings. DCC 2004
Print_ISBN :
0-7695-2082-0
DOI :
10.1109/DCC.2004.1281501
