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We present two new approaches based on color histogram indexing for content-based retrieval in image databases. Since the high computational complexity has been one of the main barriers towards the use of similarity measures such as histogram intersection in large databases, we propose a hierarchical indexing scheme where computationally efficient features are used to subset the images before more sophisticated techniques are applied for precise retrieval. The use of histograms at different color resolutions as filtering and matching features in a hierarchical scheme is studied. In the second approach, a multiresolution representation of the histogram using the indices and signs of its largest wavelet coefficients is examined. Excellent results have been observed using the latter method.Keywords: color indexing, color histogram, multiresolution, content-based retrieval. 0-8194-1970-2/95/$6.OO Downloaded From: http://proceedings.spiedigitallibrary.org/ on 05/18/2015 Terms of Use: http://spiedl.org/terms

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We introduce the concept of semantic multicast to implement a large-scale shared interaction infrastructure providing mechanisms for collecting, indexing, and disseminating the information produced in collaborative sessions. This infrastructure captures the interactions between users as video, text, audio, and other data streams and promotes a philosophy of ltering, archiving, and correlating collaborative sessions in user and context sensitive groupings. The semantic multicast service e ciently disseminates relevant information to every user engaged in the collaborative session, making the aggregated streams of the collaborative session available to the correct users at the right amount of detail. This contextual focus is accomplished by i n troducing proxy servers to gather, annotate, and lter the streams appropriate for speci c interest groups. Users are subscribed to appropriate proxies, based on their pro les, and the collaborative session becomes a m ulti-level multicast of data from sources through proxies and to user interest groups. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or direct commercial advantage and that copies show this notice on the rst page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works, requires prior speci c permission and or a fee. Permissions may be requested from Publications Dept, ACM Inc., 1515 Broadway, New York, NY 10036 USA, fax +1 212 869-0481, or permissions@acm.org. Additional Key Words and Phrases: Collaboration data models, content extraction, distributed multimedia archives, incremental clustering, information dissemination, information ltering, information sharing, IP multicast, metadata, proxy-based services, user pro les 1. INTRODUCTION Current networked collaborative activities usually consist of an interaction stream" broadcast over a single network channel if users have a n i n terest in the session, they must participate in the entire event and process all broadcast information. Furthermore, if a collaborative session involves multiple interactions from interrelated working groups, a user must participate in the full broadcast from all groups to learn the interrelationship. This model of collaboration only supports two modes of operation: a user either actively participates in the session or the user does not participate at all. In actuality though, a collaborative session is one in which users participate in varying levels at varying times and multiple working groups, or interaction streams, concurrently overlap. Minimal support, if any, exists to decompose collaborative sessions among related working groups, lter and share information between groups, e ciently recall spe...

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