A computer data base system for indexing research papers /

"Issued October 1980."

Final report, indexing aids. Documentation, inc., Bethesda, Md., contract AF 30(602)-2616. Prepared for Rome Air Development Center, Air Force Systems Command, U.S. Air Force, Griffiss Air Force Base

AD 294 859.

Studies in coordinate indexing.

v.4.The mechanization of data retrieval.--v.5.Emerging solutions for mechanizing the storage and retrieval of information.--v.6.The coming age of information technology.

Report on indexing federal programs.

Mode of access: Internet.

Word correlation and automatic indexing, phase IIA final report, C82-2UA. Submitted to the Council on Library Resources.

Includes bibliography.

DTIC retrieval and indexing terminology /

"January 1987."

Indexing; principles, rules and examples.

Also issued, with Library school bulletins 20 and 23, as appendix 3, of the State library report for 1905.

Medical subject headings, tree annotations, 1978 : technical notes, Medlars indexing instructions /

"PB 271 246."

Technical notes, MEDLARS indexing instructions, supplement, tumor key /

"Supersedes Technical Notes: MEDLARS Indexing Instructions, Supplement I (Tumor Key), PB-245 412. Supplements 1981 Medical Subject Headings."

Water resources thesaurus; a vocabulary for indexing and retrieving the literature of water resources research and development.

"Developed under a contract with the Science Information Exchange (SIE), Smithsonian Institution."

The Hull letters, printed from a collection of original documents found among the borough archives in the town hall, Hull, 1884, during the progress of the work of indexing.

Mode of access: Internet.

Indexing with the consumer price index : problems and alternatives /

"June 1981"--cover

Guide to abstracting & indexing at the Technical Information Center /

"TID-4583."

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Hierarchical indexing structure for efficient similarity search in video retrieval

With the rapid increase in both centralized video archives and distributed WWW video resources, content-based video retrieval is gaining its importance. To support such applications efficiently, content-based video indexing must be addressed. Typically, each video is represented by a sequence of frames. Due to the high dimensionality of frame representation and the large number of frames, video indexing introduces an additional degree of complexity. In this paper, we address the problem of content-based video indexing and propose an efficient solution, called the Ordered VA-File (OVA-File) based on the VA-file. OVA-File is a hierarchical structure and has two novel features: 1) partitioning the whole file into slices such that only a small number of slices are accessed and checked during k Nearest Neighbor (kNN) search and 2) efficient handling of insertions of new vectors into the OVA-File, such that the average distance between the new vectors and those approximations near that position is minimized. To facilitate a search, we present an efficient approximate kNN algorithm named Ordered VA-LOW (OVA-LOW) based on the proposed OVA-File. OVA-LOW first chooses possible OVA-Slices by ranking the distances between their corresponding centers and the query vector, and then visits all approximations in the selected OVA-Slices to work out approximate kNN. The number of possible OVA-Slices is controlled by a user-defined parameter delta. By adjusting delta, OVA-LOW provides a trade-off between the query cost and the result quality. Query by video clip consisting of multiple frames is also discussed. Extensive experimental studies using real video data sets were conducted and the results showed that our methods can yield a significant speed-up over an existing VA-file-based method and iDistance with high query result quality. Furthermore, by incorporating temporal correlation of video content, our methods achieved much more efficient performance.