11,807 research outputs found
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
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Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
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Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
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First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
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Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
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Third, we add the functionality to process the users' relevance feedback.\ud
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We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
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We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
A query description model based on basic semantic unit composite Petri-Net for soccer video
Digital video networks are making available increasing amounts of sports video data. The volume of material on offer means that sports fans often rely on prepared summaries of game highlights to follow the progress of their favourite teams. A significant application area for automated video analysis technology is the generation of personalized highlights of sports events. One of the most popular sports around world is soccer. A soccer game is composed of a range of significant events, such as goal scoring, fouls, and substitutions. Automatically detecting these events in a soccer video can enable users to interactively design their own highlights programmes. From an analysis of broadcast soccer video, we propose a query description model based on Basic Semantic Unit Composite Petri-Nets (BSUCPN) to automatically detect significant events within soccer video. Firstly we define a Basic Semantic Unit (BSU) set for soccer videos based on identifiable feature elements within a soccer video, Secondly we design Composite Petri-Net (CPN) models for semantic queries and use these to describe BSUCPNs for semantic events in soccer videos. A particular strength of this approach is that users are able to design their own semantic event queries based on BSUCPNs to search interactively within soccer videos. Experimental results
based on recorded soccer broadcasts are used to illustrate the potential of this approach
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval
Extending the 5S Framework of Digital Libraries to support Complex Objects, Superimposed Information, and Content-Based Image Retrieval Services
Advanced services in digital libraries (DLs) have been developed and widely used to address the required capabilities of an assortment of systems as DLs expand into diverse application domains. These systems may require support for images (e.g., Content-Based Image Retrieval), Complex (information) Objects, and use of content at fine grain (e.g., Superimposed Information). Due to the lack of consensus on precise theoretical definitions for those services, implementation efforts often involve ad hoc development, leading to duplication and interoperability problems. This article presents a methodology to address those problems by extending a precisely specified minimal digital library (in the 5S framework) with formal definitions of aforementioned services. The theoretical extensions of digital library functionality presented here are reinforced with practical case studies as well as scenarios for the individual and integrative use of services to balance theory and practice. This methodology has implications that other advanced
services can be continuously integrated into our current extended framework whenever they are identified. The theoretical definitions and case study we present may impact future development efforts and a wide range of digital library researchers, designers, and developers
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