5416143
A clustering based approach to efficient image retrieval
This paper addresses the issue of effective and efficient content based image retrieval by presenting a novel indexing and retrieval methodology that integrates color, texture, and shape information for the indexing and retrieval, and applies these features in regions obtained through unsupervised segmentation, as opposed to applying them to the whole image domain. In order to address the typical color feature "inaccuracy" problem in the literature, fuzzy logic is applied to the traditional color histogram to solve for the problem to a certain degree. The similarity is defined through a balanced combination between global and regional similarity measures incorporating all the features. In order to further improve the retrieval efficiency, a secondary clustering technique is developed and employed to significantly save query processing time without compromising the retrieval precision. An implemented prototype system has demonstrated a promising retrieval performance for a test database containing 2000 general-purpose color images, as compared with its peer systems in the literature.
5969857
A data mining approach to modeling relationships among categories in image collection
This paper proposes a data mining approach to modeling relationships among categories in image collection. In our approach, with image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called α-Semantics Graph, to discover the hidden semantic relationships among the semantic categories embodied in the image collection. With the α-Semantics Graph, each semantic category is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and semantic overlap among the categories in the feature space. The model is utilized in the semantics-intensive image retrieval application. An algorithm using the classification accuracy measures is developed to combine the built classification tree with the fuzzy set modeling method to deliver semantically relevant image retrieval for a given query image. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and the image retrieval prototype system utilizing the derived model is promising both in effectiveness and efficiency.
1852867
Semantic repository modeling in image database
This work is about content based image database retrieval, focusing on developing a classification based methodology to address semantics-intensive image retrieval. With self organization map based image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the image database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based image retrieval system in the literature both in effectiveness and efficiency.
3159534
FAST: Toward more effective and efficient image retrieval
This paper focuses on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region-based approach to improving the retrieval effectiveness and robustness; (2) development of a new hierarchical indexing structure and the corresponding hierarchical, elimination-based A* retrieval (HEAR) algorithm to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case; (3) employment of user relevance feedback to tailor the effective retrieval to each user's individualized query preference through the novel indexing tree pruning (ITP) and adaptive region weight updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds great promise in delivering fast and semantics-tailored image retrieval in CBIR.
16733151
A unified fuzzy feature indexing scheme for region based online image querying
We describe a novel indexing and retrieval methodology integrating color, texture and shape information for content-based image retrieval in online image databases. This methodology, called PicSearcher, applies unsupervised image segmentation to partition an image into a set of regions, then fuzzy color histogram as well as fuzzy texture and shape properties of each region is calculated to be part of their signatures. The fuzzification procedures resolve the recognition uncertainty stemming from color quantization and human perception of colors. At the same time, this unified fuzzy scheme incorporates the segmentation-related uncertainties into the retrieval algorithm. Then an adaptive and effective measure for the overall similarity between images is developed by integrating properties of all the regions in the image. An implemented prototype system of PicSearcher has demonstrated a promising retrieval performance for an online test database containing 10,000 general-purpose color images, as compared with its peer systems in the literature.
62753126
A Bayesian framework for automatic concept discovery in image collections
This paper develops a Bayesian framework for automatic hidden semantic concept discovery to address effective semantics-intensive content-based image retrieval. Each image in the database is segmented to regions associated with homogenous color, texture, and shape features. By employing self-organization map learning, a uniform and sparse region-based representation is obtained. With this representation a probabilistic model based on the statistical-hidden-class assumptions of the image database is developed, to which expectation-maximization technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative vector, to the discovered semantic concepts. The proposed approach has a solid statistical foundation and the experimental evaluations on a database of 10,000 general-purposed images demonstrate its promise of the retrieval effectiveness.
28725102
A Framework for Component Reuse in a Metamodelling-Based Software Development
Reuse forms one key mechanism in improving system development productivity and quality. In this paper we suggest a systematic framework for reuse processes, which is driven by ideas and concepts based on metamodelling. The framework enables us to consider reuse in a comprehensive manner and to develop support for reuse processes within a metaCASE environment. By utilising metamodelling concepts we expand reuse situations to cover both design artefacts and method components. Accordingly, the reuse framework consists of a system development process model, a type level hierarchy, a classification of reuse situation types, a component granularity model, and a model of reuse operations. We demonstrate the usefulness of the framework by analysing reuse support in a metaCASE environment: MetaEdit+. We also examine how the framework can help to strategise over possible software development scenarios (patterns) that involve various levels of reuse in situations where software requirements change considerably and there is a considerable number of design assets to exploit.
18861911
Service Identification and Packaging in Service Oriented Reengineering
With the adoption to Web service technologies, more and more existing non-service-oriented software systems turn to be legacy systems. They require a service-oriented reengineering process in order to survive in serviceoriented computing environment. In this paper, we present an architecture-based service-oriented approach to support service-oriented reengineering. It integrates and reuses software components as services over Internet by wrapping underlying computing models with XML. Service identification and service packaging processes are described in details, which are the keys to serviceoriented reengineering. Target services are defined according to service identification, which is based on a comparison of analyses in both problem domain and legacy systems. Clustering techniques are applied in service identification process to analyse recovered architectural information and to identify related modules. Service packaging is benefit from architecture-based development and reuse.
7609831
Learning cross-document structural relationships using boosting
Multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that uses boosting to classify CST relationships between sentence pairs extracted from topically related documents. We show that the binary classifier for determining existence of structural relationships significantly outperforms the baseline. We also achieve promising results on the multi-class case in which the full taxonomy of relationships are considered.
7981159
Extraction of temporally correlated sources with its application to non-invasive fetal electrocardiogram extraction
This letter addresses the problem of fetal electrocardiogram (FECG) extraction. A class of source extraction algorithms is proposed and it uses a combination of two different approaches: Barros's source extraction approach and Hyvarinen's fixed-point approach. The proposed algorithms can rapidly extract the desired FECG with little noise, and can work well in some poor situations. The validity and performance of the algorithms are confirmed by extensive computer simulations and experiments on real-world data.
18878687
Directional Coherence Interpolation For Three-Dimensional Gray-Level Images
A novel three-dimensional gray-level interpolation method called the Directional Coherence Interpolation (DCI) is presented in the paper. The principal advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpolates the missing image data along the maximum coherence directions (MCD), which are estimated from the local image intensity yet constrained by a generic smoothness term. In order to further improve both the algorithm efficiency and robustness, we also propose to apply a pyramidal search strategy for MCD estimation. This coarse-to-fine scheme requires less computation time by starting with the reduced amount of data and propagating searching results to finer resolutions. DCI can incorporate image shape and structure information without the prior requirement of explicit representation of object boundary/surface. Extensive experiments were performed on both synthetic and real medical images to evaluate the proposed approaches. The experimental results show that the proposed methods are able to handle general object interpolation, while achieving both accuracy and efficiency in interpolation compared with the existing techniques.