descriptions, and specificity one of index terms. They are most clearly illustrated by a simple keyword or descriptor system. In this case the exhaustivity of a document description is the coverage of its various topics given by the terms assigned to it; and the specificity of an individual term is the level

A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries

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

The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend

, 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 distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we

The key ideas in the theory of broadcast channels are illustrated by discussing some of the progress toward finding the capacity region. The capacity region is still unknown. Index Terms---Binning, broadcast channel, capacity, degraded broadcast channel, feedback capacity, Slepian

for efficient retrieval from a database of more than 1,000 images. Experimental results show correct retrieval in the case of partial visibility, similarity transformations, extraneous features, and small perspective deformations. Index Terms—Image retrieval, image indexing, graylevel invariants, matching

by 200-300 of the largest singular vectors are then matched against user queries. We call this retrieval method Latent Semantic Indexing (LSI) because the subspace represents important associative relationships between terms and documents that are not evident in individual documents. LSI is a completely

, semiboundedness, and boundedness. Index Terms-Divergence, dissimilarity measure, discrimination in-formation, entropy, probability of error bounds. I.

the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation. Index Terms—Computer programming costs, cost models, management decision