An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation which may be efficiently processed to determine correspondence. Applications of the interface and system include a VCR, medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Ergonomic man-machine interface incorporating adaptive pattern recognition based control system

For thousands of years people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information; and finding useful information from such collections became a necessity. The field of Information Retrieval (IR) was born in the 1950s out of this necessity. Over the last forty years, the field has matured considerably. Several IR systems are used on an everyday basis by a wide variety of users. This article is a brief overview of the key advances in the field of Information Retrieval, and a description of where the state-of-the-art is at in the field.

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Modern Information Retrieval : A Brief Overview

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation that may be efficiently processed to determine correspondence. Applications of the interface and system include a video cassette recorder (VCR), medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Adaptive pattern recognition based control system and method

The paper combines a comprehensive account of the probabilistic model of retrieval with new systematic experiments on TREC Programme material. It presents the model from its foundations through its logical development to cover more aspects of retrieval data and a wider range of system functions. Each step in the argument is matched by comparative retrieval tests, to provide a single coherent account of a major line of research. The experiments demonstrate, for a large test collection, that the probabilistic model is eAective and robust, and that it responds appropriately, with major improvements in performance, to key features of retrieval situations. Part 1 covers the foundations and the model development for document collection and relevance data, along with the test apparatus. Part 2 covers the further development and elaboration of the model, with extensive testing, and briefly considers other environment conditions and tasks, model training, concluding with comparisons with other approaches and an overall assessment. Data and results tables for both parts are given in Part 1. Key results are summarised in Part 2. 7 2000 Elsevier Science Ltd. All rights reserved.

A probabilistic model of information retrieval: development and comparative experiments

Color histograms are used to compare images in many applications. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, so images with very different appearances can have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels; this could have a similar color histogram to a picture with a single large red object. We describe a histogram-based method for comparing images that incorporates spatial information. We classify each pixel in a given color bucket as either coherent or incoherent, based on whether or not it is part of a large similarly-colored region. A color coherence vector (CCV) stores the number of coherent versus incoherent pixels with each color. By separating coherent pixels from incoherent pixels, CCV’s provide finer distinctions than color histograms. CCV’s can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried for the images with the most similar CCV’s in under 2 seconds. We show that CCV’s can give superior results to color his∗To whom correspondence should be addressed tograms for image retrieval.

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Comparing images using color coherence vectors

A significant new speech corpus of British English has been recorded at Cambridge University. Derived from the Wall Street Journal text corpus, WSJCAMO constitutes one of the largest corpora of spoken British English currently in existence. It has been specifically designed for the construction and evaluation of speaker-independent speech recognition systems. The database consists of 140 speakers each speaking about 110 utterances. This paper describes the motivation for the corpus, the processes undertaken in its construction and the utilities needed as support tools. All utterance transcriptions have been verified and a phonetic dictionary has been developed to cover the training data and evaluation tasks. Two evaluation tasks have been defined using standard 5000 word bigram and 20000 word trigram language models. The paper concludes with comparative results on these tasks for British and American English.

WSJCAMO: a British English speech corpus for large vocabulary continuous speech recognition

Recent years have seen a rapid increase in the availability and use of multimedia applications. These systems can generate large amounts of audio and video data which can be expensive to store and unwieldy to access. The Video Mail Retrieval (VMR) project at Cambridge University and Olivetti Research Limited (ORL), Cambridge, UK, is addressing these problems by developing systems to retrieve stored video material using the spoken audio soundtrack [1, 16]. Speci cally, the project focuses on the content-based location, retrieval, and playback of potentially relevant data. The primary goal of the VMR project is to develop a video mail retrieval application for the Medusa multimedia environment developed at ORL. Previous work on the VMR project demonstrated practical retrieval of audio messages using speech recognition for content identi cation [8, 4]. Because of the limited number of available audio messages, a much larger archive of television news broadcasts (along with accompanying subtitle transcriptions) is currently being collected. This will serve as a testbed for new methods of storing and accessing large amounts of audio/video data. The enormous potential size of the news broadcast archive dramatically illustrates the need for ways of automatically nding and retrieving information from the archive. Quantitative experiments demonstrate that Information Retrieval (IR) methods developed for searching text archives can accurately retrieve multimedia data, given suitable subtitle transcriptions. In addition, the same techniques can be used to rapidly locate interesting areas within an individual news broadcast. Although large multimedia archives will be more common in the future, today they require a specialised and highperformance hardware infrastructure. The work presented here relies on the the Medusa system developed at ORL, which includes distributed, high-capacity multimedia repositories. This paper begins with an overview of the ORL Medusa technology. Subsequent sections describe the collection and storage of a BBC television broadcast news archive, a retrieval methodology for location of potentially relevant sections in response to users' requests, and a graphical user interface for content-based retrieval and browsing of news f g

Automatic content-based retrieval of broadcast news

This paper presents domain-independent methods of spoken document retrieval. Both a continuous-speech large vocabulary recognition system, and a phone-lattice word spotter, are used to locate index units within an experimental corpus of voice messages. Possible index terms are nearly unconstrained; terms not in a 20,000 word recognition system vocabulary can be identified by the word spotter at search time. Though either system alone can yield respectable retrieval performance, the two methods are complementary and work best in combination. Different ways of combining them are investigated, and it is shown that the best of these can increase retrieval average precision for a speakerindependent retrieval system to 85% of that achieved for full-text transcriptions of the test documents.

Retrieving spoken documents by combining multiple index sources

This paper presents recent work on a multimedia retrieval project at Cambridge University and Olivetti Research Limited (ORL). We present novel techniques that allow extremely rapid audio indexing, at rates approaching several thousand times real time. Unlike other methods, these techniques do not depend on a fixed vocabulary recognition system or on keywords that must be known well in advance. Using statistical methods developed for text, these indexing techniques allow rapid and efficient retrieval and browsing of audio and video documents. This paper presents the project background, the indexing and retrieval techniques, and a video mail retrieval application incorporating content-based audio indexing, retrieval, and browsing.

Open-vocabulary speech indexing for voice and video mail retrieval

This paper describes experiments in the retrieval of spoken documents in multimedia systems. Speech documents pose a particular problem for retrieval since their words as well as contents are unknown. The work reported addresses this problem, for a video mail application, by combining state of the art speech recognition with established document retrieval technologies so as to provide an effective and efficient retrieval tool. Tests with a small spoken message collection show that retrieval precision for the spoken file can reach 90% of that obtained when the same file is used, as a benchmark, in text transcription form.

J.T. Foote

Papers

WSJCAMO: a British English speech corpus for large vocabulary continuous speech recognition

Automatic content-based retrieval of broadcast news

Retrieving spoken documents by combining multiple index sources

Open-vocabulary speech indexing for voice and video mail retrieval

Experiments in spoken document retrieval