Learning in an image retrieval scheme that uses hidden annotations is investigated. Compared with low level visual features that are straightforward functions of the raw pixel values, hidden annotations are higher level hidden semantic attributes. In the proposed scheme, a small set of images

The Bayesian relevance-feedback approach introduced with the PicHunter system [5] is extended to include hidden semantic attributes. The general approach is motivated and experimental results are presented that demonstrate significant reductions in search times (28-32%) using these annotations. 1

The Bayesian relevance-feedback approach introduced with the PicHunter system [5] is extended to include hidden semantic attributes. The general approach is motivated and experimental results are presented that demonstrate significant reductions in search times (28-32%) using these annotations. 1

Raster graphics or bitmap ◮ Made up of individual pixels, resolution dependent ◮ Resizing reduces quality ◮ Minimal support for transparency ◮ Conversion to vector is difficult ◮ File types:.jpg,.gif,.tif, and.bmp Vector graphics or line art ◮ Created mathematically w/o the use of pixels ◮ High resolution ◮ Scalable to any size w/o pixelation or quality loss ◮ Conversion to raster is easy

on the characterizing stochastic process is computed. A high likelihood indicates a strong association. In our experimental implementation, we focus on a particular group of stochastic processes, that is, the two-dimensional multiresolution hidden Markov models (2-D MHMMs). We implemented and tested our ALIP (Automatic

by refining a query. Second, an entropy-minimizing display algorithm is described that attempts to maximize the information obtained from a user at each iteration of the search. Third, PicHunter makes use of hidden annotation rather than a possibly inaccurate/inconsistent annotation structure that the user

We present a technique for automatic induction of slot annotations for subcategorization frames, based on induction of hidden classes in the EM framework of statistical estimation. The models are empirically evalutated by a general decision test. Induction of slot labeling for subcategorization

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One of the most basic tasks of bioinformatics is to identify features in a biological sequence. Whether those features are the binding sites of a protein, the regions of a DNA sequence that are most subject to selective pressures, or coding sequences found in an expressed sequence tag, this phase is fundamental to the process of sequence

Motivation: Phylogenetic shadowing is a comparative genomics principle that allows for the discovery of conserved regions in sequences from multiple closely related organisms. We develop a formal probabilistic framework for combining phylogenetic shadowing with feature-based functional annotation