The lifecycle of document management applications typically comprises a set of loosely coupled subsystems that provide capture, index, search, workflow, fulfillment and archival features. However, there exists no standard model for composing these elements together to instantiate a complete application. Therefore, every application invariably incorporates custom application code to provide the linkages between each of these loosely coupled subsystems. This paper proposes a model-based approach to instantiating document management applications. An Eclipse Modeling Framework (EMF) based model is used to formalize the variable elements in the document management applications. The modeling tool supports the instantiation of an EMF model for every new application and supports the generation of runtime artifacts - this includes code, XML configurations, scripts and business logic. This approach to creating new instances of document management applications with a formal EMF model has been validated with a real-world document management application.

Today, XML is increasingly becoming a standard for representation of semi-structured information such as documents that combines content and metadata. Typical document management applications include document representation, authoring, validation, and document routing in support of a business process. We propose a framework for intelligent document routing that exploits and extends XML technologies to automate dynamic document routing and real-time update of business routing logic. The document-routing logic is stored in a secure repository and executed by a business rules engine. During rule execution, the input parameters of each business rule are bound with the data from each inbound XML document. This document routing framework is validated in a real-world implementation with reduced development cost, accelerated rule update cycle and simplified administration efforts.

Document analysis research typically focuses on document image understanding or classic problems in text classification, clustering, summarization and discovery. While that is an important aspect of document management, in practice, documents lifecycles are often determined by the context of the business process that they are relevant to. It therefore becomes necessary for the document analysis techniques to recognize and leverage the contextual information provided by a supporting schema and business process. This paper presents an intelligent document management framework with relevant document analysis, metadata extraction, and business process association algorithms and methodology. The architecture supporting this framework seamlessly integrates a runtime environment with an authoring environment by combining relational data modeling tools with document classification techniques. The runtime environment accepts incoming documents, classifies the document, extracts metadata and executes customized business logic. The authoring environment supports the association of a class of documents with a relational document schema, identification of attribute values that must be extracted automatically, generation of relevant business logic, and deployment of authoring artifacts into the runtime architecture. We demonstrate the use of this framework with representative real-world document transformative applications.

The daily use of Internet-based services is involved with hundreds of different tasks being performed by multiple users. A single task is typically involved with a sequence of Web URLs invocation. We study the problem of pattern detection in Web logs to identify tasks performed by users, and analyze task trends over time using a grammar-based framework. Our results are demonstrated on a corporate Intranet portal application with 7000 users over a 6 week period and demonstrate compelling business value from this high-level task analysis.

Phonetic speech retrieval is used to augment word based retrieval in spoken document retrieval systems, for in and out of vocabulary words. In this paper, we present a new indexing and ranking scheme using metaphones and a Bayesian phonetic edit distance. We conduct an extensive set of experiments using a hundred hours of HUB4 data with ground truth transcript and twenty-four thousands query words. We show improvement of up to 15% in precision compare to results obtained speech recognition alone, at a processing time of 0.5 Sec per query.

Speech is a tantalizing mode of human communication. On the one hand, humans understand speech with ease and use speech to express complex ideas, information, and knowledge. On the other hand, automatic speech recognition with computers is still very hard, and extracting knowledge from speech is even harder. In this paper we motivate the study of speech as a knowledge resource and briefly survey a family of related applications and systems being developed at IBM Research aimed towards the goal of exploiting speech as a knowledge resource.

This paper addresses the problem of automatic detection of salient video segments for real-world applications such as corporate training based on associated speech transcriptions. We present a novel segmentation algorithm based on automatic speech recognition (ASR) applied to the audio track of the video. Our feature set consists of word n-grams extracted from the imperfect speech transcriptions. We use a two-pass algorithm that combines a boundary-based method with a content-based method. In the first pass, we analyze the temporal distribution and the rate of arrival of features to compute an initial segmentation. In the second pass, we detect changes in content-bearing words by using the content-bearing features as queries in an information retrieval system. The content-based second pass validates the initial segments and merges them as needed. Variations in the structure of the audio/video content, and the accuracy of ASR have an impact on the feasibility of the segmentation task. For realistic data we observe that we can identify content-rich segments of the audio. In the best scenario a high-level table-of-contents is generated and in the worse scenario a single salient segment is identified. We illustrate the algorithm in detail with some examples and validate the data with manual segmentation boundaries.

algorithm for the segmentation of an audio/video source into topically cohesive segments based on automatic speech recognition (ASR) transcriptions is presented. A novel two-pass algorithm is described that combines a boundary-based method with a content-based method. In the first pass, the temporal proximity and the rate of arrival of ngram features is analyzed in order to compute an initial segmentation. In the content- based second pass, changes in content-bearing words are detected by using the ngram features as queries in an information-retrieval system. The second pass validates the initial segments and merges them as needed. Feasibility of the segmentation task can vary enormously depending on the structure of the audio content, and the accuracy of ASR. For real-world corporate training data our method identifies, at worst, a single salient segment of the audio and, at best, a high-level table-of-contents. We illustrate the algorithm in detail with some examples and validate the results with segmentation boundaries generated manually.

The detection and recognition of events is a challenging problem in video databases. It involves cross-linking and combining information available in multiple modalities such as audio, video and associated text metadata. CueVideo is a system designed for the discovery and recognition of specific events called topics of discussion through advanced video summarization and cross-modal indexing. It supports search for relevant video content through several modes of video summarization including storyboards, moving storyboards and time-scale modified audio summarization. It also enables the recognition and indexing of topical events through cross-model search of audio and video content based on text and image queries respectively.

The detection of events is essential to high-level semantic querying of video databases. It is also a very challenging problem requiring the detection and integration of evidence for an event available in multiple information modalities, such as audio, video and language. This paper focuses on the detection of specific types of events, namely, topic of discussion events that occur in classroom/lecture environments. Specifically, we present a query-driven approach to the detection of topic of discussion events with foils used in a lecture as a way to convey a topic. In particular, we use the image content of foils to detect visual events in which the foil is displayed and captured in the video stream. The recognition of a f0il in video f~ames exploits the color and spatial layout of regions on foils using a technique called region hashing. Next, we use the textual phrases listed on a foil as an indication of a topic, and detect topical audio events as places in the audio track where the best evidence for the topical phrases was heard. Finally, we use a probabilistic model of event likelihood to combine the results of visual and audio event detection that exploits their time cooccurrence. The resulting identification of topical events is evaluated in the domain of classroom lectures and talks.

Combined word-based index and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary retrieval problem. However, a known problem with phonetic recognition is its limited accuracy in comparison with word level recognition. We propose a novel method for phonetic retrieval in the CueVideo system based on the probabilistic formulation of term weighting using phone confusion data in a Bayesian framework. We evaluate this method of spoken document retrieval against word-based retrieval for the search levels identified in a realistic video-based distributed learning setting. Using our test data, we achieved an average recall of 0.88 with an average precision of 0.69 for retrieval of out-of-vocabulary words on phonetic transcripts with 35% word error rate. For in-vocabulary words, we achieved a 17% improvement in recall over word-based retrieval with a 17% loss in precision for word error rites ranging from 35 to 65%.

No abstract available.

The role of audio in the context of multimedia applications involving video is becoming increasingly important. Many efforts in this area focus on audio data that contains some built-in semantic information structure such as in broadcast news, or focus on classification of audio that contains a single type of sound such as cleaar speech or clear music only. In the CueVideo system, we detect and classify audio that consists of mixed audio, i.e. combinations of speech and music together with other types of background sounds. Segmentation of mixed audio has applications in detection of story boundaries in video, spoken document retrieval systems, audio retrieval systems etc. We modify and combine audio features known to be effective in distinguishing speech from music, and examine their behavior on mixed audio. Our preliminary experimental results show that we can achieve a classification accuracy of over 80% for such mixed audio. Our study also provides us with several helpful insights related to analyzing mixed audio in the context of real applications.

No abstract available.

The development of highly interactive software systems with complex user interfaces has become increasingly common, where prototypes are often used as a vehicle for demonstrating visions of innovative systems. Given this trend, it is important for new technology to be based on flexible architectures that do not mandate the understanding of all complexities inherent in a system. In this context, we share our experience with developing an object oriented framework for a specific new technology, i.e. speech recognition. We describe the benefits of the object oriented paradigm rich with design patterns, which provide a natural way to model complex concepts and capture system relationships effectively, along with achieving a high level of software reuse.

Article Key to effective video retrieval: effective cataloging and browsing Share on Authors: Dulce Ponceleon IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Savitha Srinivasan IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Arnon Amir IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Dragutin Petkovic IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Dan Diklic School of Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CA School of Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CAView Profile Authors Info & Claims MULTIMEDIA '98: Proceedings of the sixth ACM international conference on MultimediaSeptember 1998 Pages 99–107https://doi.org/10.1145/290747.290760Published:01 September 1998 40citation1,156DownloadsMetricsTotal Citations40Total Downloads1,156Last 12 Months13Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access