Sentence examples for visual retrieval from inspiring English sources

Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered.

In a previous study [21], a two-stepped recovery of multimedia information was developed, with content-based visual information retrieval being performed only in the second stage.

In this paper the visual information retrieval project VizIR is presented.

Content-based visual information retrieval occurs on a reduced cluster of the collection, thus enhancing recovery efficiency.

Implications for the development of models for visual information retrieval, and for the design of Web search engines are discussed.

Such results encourage the continuation of this work, to solve a question that significantly enhances the quality of content-based visual information retrieval systems.

The first step consists of searching the collection for a top-K cluster, and only after this, the cluster formation was compared by content-based visual information retrieval.

The goal of the project is the implementation of an open visual information retrieval (VIR) prototype as basis for further research on major problems of VIR.

In this article, we first describe how users' trails in sessions of an experimental study of visual information retrieval can be characterized by Hidden Markov Models.

Therefore, this study proposes an approach for automatic and online estimation of the similarity threshold value, to be specifically used by content-based visual information retrieval system (image and video) search engines.

Among studies aimed at improving content-based visual information retrieval systems, this one particularly addresses effectiveness improvement (i.e. system's quality improvements through search, setting the automatic and online similarity threshold to be adopted).