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

indexed by distinctions-i.e., assumptions of inequality among names. These theories are based upon a semantics in terms of a labeled transition system and a notion of strong bisimulation, both of which are expounded in detail in a companion paper. We also report briefly on work-in-progress based upon

crucially on the choice of effective term-weighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared.

, 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

's presence in many arrays of an array set allows the probe-sensitivity index to be estimated accurately. We extended the model to obtain expression values for PM-only arrays, and found that the 20-probe PM-only model is comparable to the 10-probe PM/MM difference model, in terms of the expression

with techniques from robust statistics and treat occluded regions on the object as statistical outliers. Throughout, we present experimental results performed on live video sequences demonstrating the effectiveness and efficiency of our methods. Index Terms—Visual tracking, real-time vision, illumination, motion

saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail. Index terms: Visual attention, scene analysis, feature extraction, target detection, visual search. \Pi I. Introduction

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

for computing difference feature vectors, and the “nonface ” clusters we include in our distribution-based model, are both critical for the success of our system. Index Terms—Face detection, object detection, example-based learning, example selection, pattern recognition, view-based

-held camera. Index Terms—Autonomous vehicles, 3D/stereo scene analysis, tracking. Ç 1