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An evolutionary adaptive algorithm for solving a class of online service provider problems in a dynamical web environment is introduced. In the online service provider scenario, a system continuously generates digital products and service instances by assembling components (e.g. headlines of online newspapers, search engine query results, advertising lists) to fulfill the requirements of a market of anonymous customers. The evaluation of a service instance can only be known by the feedback obtained after delivering it to the customer over the internet or through telephone networks. In dynamic domains available components and customer/agents preferences are changing over the time. The proposed algorithm employs typical genetic operators in order to optimize the service delivered and to adapt it to the environment feedback and evolution. Differently from classical genetic algorithms the goal of such systems is to maximize the average fitness instead of determining the single best optimal service/product. Experimental results for different classes of services, online newspapers and search engines, confirm the adaptive behavior of the proposed technique.

Adaptive search engines (ASE), used in the retrieval of multimedia objects adapt their behavior depending on the user feedback in order to eventually converge to the optimal answer. The adaptive architecture has been shown to improve the performance in case of multimedia objects retrieval, when pre-indexing techniques are costly or can be applied only partially. The continuous user feedbacks on the lists of returned objects are used to filter out irrelevant objects and promote the relevant ones. This work propose an original dealer/opponent game model for ASE. The system/user interactive process which takes place in ASE can be modeled as a discovery game between a dealer, the user community which holds a secret consisting in the optimal answer to a query, and an opponent, i.e. the system, which tries to discover the secret by submitting tentative solutions on which it receives the user/dealer feedback. It is shown how the complexity of the game can be related to known games. An evolutionary approach to solve the ASE game is also presented. Experimental results shows convergence to the optimal solution with acceptable performance for real domain size. The proposed schema is quite general and can fit other adaptive search architectures which appear in e-business and e-commerce applications.

Face recognition can be considered as a one-class classification problem and associative memory (AM) based approaches have been proven efficient in previous studies. In this paper, a kernel associative memory (KAM) based face recognition scheme with a multiscale Gabor transform, is proposed, in our method, face images of each person are first decomposed into their multiscale representations by a quasi-complete Gabor transform, which are then modelled by kernel associative memories, The pyramidal multi-scale Gabor wavelet transform not only provides a very efficient implementation of Gabor transform in spatial domain, but also permits a fast reconstruction. In the testing phase, a query face image is also represented by a Gabor multiresolution pyramid and the recalled results from different KAM models corresponding to even Gabor channels are then simply added together to provide a reconstruction. The recognition scheme was thoroughly tested using several benchmark face datasets, including the AR faces, UMIST faces, JAFFE faces and Yale A faces. The experiment results have demonstrated strong robustness in recognizing faces under different conditions, particularly the poses alterations, varying occlusions and expression changes.

While text-oriented document searching are relatively mature on the Internet, image searching, which requires much more than text matching, significantly lags behind. The use of image search engines significantly enlarges the scope of images to users accessibility. This paper provides an understanding of current technologies in image searching on the Internet, and points to future areas of improvement for multimedia applications. We develop a systematic set of image queries to assess the competence and performance of the major image search engines. We find that current technology is only able to deliver an average precision of around 42% and an average recall of around 12%, while the best performers are capable of producing over 70% for precision and around 27% for recall. The reasons for such differences, and mechanisms for search improvement, are also indicated.

This paper focuses on context-based image indexing and retrieval with XML representations (CIIRX), and the development of improved methods for the indexing and retrieval of images using the MPEG-7 standard structured annotations. These methods involve the development of techniques to enable the elements of an image to be categorised on the basis of their relative importance within the image. We propose the indexing of images with structured annotations, which can be used as a description in an XML document database. We also describe the use of JSP in retrieving images from the database, which allows relevant images to be identified and retrieved accurately and efficiently.

The Internet world makes increasing use of XML-based technologies. In multimedia data indexing and retrieval, the MPEG-7 standard for Multimedia Description Scheme is specified using XML. The flexibility of XML allows users to define other markup semantics for special contexts, construct data-centric XML documents, exchange standardized data between computer systems, and present data in different applications. In this paper, the Inverted Image Indexing paradigm is presented and modeled using XML Schema.

Visual information systems contain a substantial amount of non-alphanumeric information, and represent a radical departure from the largely text-based paradigm of conventional information systems. For the effective operation of an information system, data search and management mechanisms play a pivotal role, and a key requirement of such a system is the ability to search and locate information much more efficiently than a manual approach. However, for a visual information system where most data and relationships are non-alphanumeric, locating the correct information poses a new challenge. In particular, the database characteristics and storage structures required to support a visual information system are substantially different from those for a conventional information system. Here, the principal functions and components of a database management system for the effective management of visual information are presented.

A multi-level hierarchical storage system (HSS) is able to provide large storage capacity for multimedia data at a more economical cost than disk only systems. The retrieval of multimedia objects from the tertiary storage however imposes substantial performance constraints. As many multimedia applications access objects sequentially following a predetermined order, we have designed a concurrent striping method that store cold objects following this order on the media units. We have carried out simulations to study its performance, and it is observed that this method can improve the system throughput, stream response time, and disk buffer usage.

Queries containing aggregate functions often combine multiple tables through join operations. We call these queries "aggregate-join" queries. In parallel processing of such queries, it must be decided which attribute to be used as a partitioning attribute, particularly join attribute or group-by attribute. Based on the partitioning attribute, we discuss three parallel aggregate-join query processing methods, namely: Join Partition Method (JPM), Aggregate Partition Method (APM), and Hybrid Partition Method (HPM). The JPM and APM models use the join attribute, and the group-by attribute, respectively, as the partitioning attribute. The HPM model combines the other two methods using a logically hybrid architecture.

The management of visual object databases is an essential function of visual information systems, and the efficient retrieval the associated data objects is vital to operation of these systems. In this paper, we use the number of visual objects retrieved per second as a measure of the throughput of visual object databases. An efficient storage organization technique for executing visual queries is studied: we group similar visual objects together as visual object groups and store each visual object group at consecutive physical locations. We propose a structured high-level indexing system that can cater for the similarity criteria employed in the application domain. Our system incorporates classification hierarchies into an indexing superstructure of metadata, context and content, using high- level content descriptions. Database performance is quantified using queuing analyses, and we show that our technique is able to significantly increase throughput and database performance.

Unstructured manual high-level indexing is too open-ended to be useful. Domain-based classification schemes reduce the variability of index captions and increase the efficiency of manual indexing by limiting the indexer's options. In this paper, we incorporate classification hierarchies into an indexing superstructure of metadata, context and content, incorporating high-level content descriptions based on the ternary fact model. An extended illustration is given to show how metadata can be automatically extracted and can subsequently help to further limit the indexer's options for context and content. Thus, this structure facilitates the indexing of high-level contents and allows semantically rich concepts to be efficiently incorporated. We also propose a form of data mining on this index to determine rules that can be used to semi-automatically (re)classify images.

Inverted file indexing and its compression have proved to be highly successful for free-text retrieval. Although the 'inverted' nature of the data structure provides an efficient mechanism for searching key words or terms in large documents, for image retrieval, the application of inverted files to the title, caption, or description of the images are not sufficient. One must be able to index and retrieve images based on the visual contents. Many content-based image retrieval techniques are used for the images as a whole picture. Analogous to free-text retrieval, a novel technique, called inverted image indexing and compression, is proposed in this paper. Similar to works in a document, each image can have multiple areas which are perceived to be meaningful visual contents. These areas are selected by users and then undergo two processes: automatic signature generation based on wavelet signatures, and users specification of high-level contents using ternary fact model. The contents in compressed form are inserted into an inverted image file. The concept of composite bitplane signature is also introduced.

The problem of content-based retrieval of images from electronic media is especially acute in contexts where the subject-matter of the images is very general. The Birkbeck system attempts to overcome the limitations of traditional keyword systems, which provide a random and undifferentiated listing of objects in a picture, by using an augmented entity-relationship model in the data-modeling process. The interface, which allows successive query reformulation, emulates some of those aspects of browsing so important in picture collections. The subject- matter of the picture is described in terms of its objects, their attributes and the relationships existing between them. The form of the description (for both storage and retrieval) is defined by the grammar of the Picture Description Language and the Picture Query Language. Descriptions which are input to the system are parsed to generate SQL statements for the RDBMS which stores the data. Pictures relevant to the user''s enquiry are retrieved and displayed in miniature for rapid consultation. To refine further the set of pictures selected the system allows query modification, which is supported by a thesaurus. Higher rates of recall and precision are achieved than are normally possible in image storage and retrieval.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper introduces Community Adaptive Search Engines (CASE) for multimedia object retrieval. CASE systems adapt their behaviour depending on the collective feedback of the users in order to eventually converge to the optimal answer. The community adaptive approach uses continuous user feedbacks on the lists of returned objects in order to filter out irrelevant objects and promote the relevant ones. An original dealer/opponent game model for CASE is proposed and an evolutionary approach to solve the CASE game is also presented. Experimental results shows convergence to the optimal solution with acceptable performance for real domain sizes.

A formula for the go-back-N ARQ (automatic repeat request) scheme applicable to Markov error patterns is derived. It is a generalization of the well-known efficiency formula p/(p+m(1-p)) (where m is the round trip delay in number of block durations and p is the block transmission success probability), and it has been successfully validated against simulation measurements. It is found that for a given error rate, error patterns having zero correlation between successive transmission generally fare better than those with negative correlation, and that error patterns with positive correlation fare better still. It is shown that the present analysis can be extended in a straightforward manner to cope with error patterns of a more complex nature. Simple procedures for numerical evaluation of efficiency under quite general error structures are presented. >