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.
62548744
Hidden semantic concept discovery in region based image retrieval
This work addresses content based image retrieval (CBIR), focusing on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented to region; associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based representation is achieved. With this representation a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which expectation-maximization (EM) 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 example, 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 effectiveness.
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.
5682515
Addressing CBIR efficiency, effectiveness, and retrieval subjectivity simultaneously
This work is about Content Based Image Retrieval (CBIR), focusing 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 algorithm (HEAR) 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 feedbacks to tailor the semantic 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 a great promise in delivering fast and semantics-tailored image retrieval in CBIR.
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.
8272087
OGSA-based grid workload monitoring
In heterogeneous and dynamic distributed systems like the grid, detailed monitoring of workload and its resulting system performance (e.g. response time) is required to facilitate performance diagnosis and adaptive performance tuning. In this paper, we present a workload monitoring infrastructure for this purpose. The infrastructure classifies and monitors workload across components in grids based on the open grid service architecture (OGSA) in an end-to-end manner. It provides the abilities to assess what components are involved in processing a work unit, to report time elapsed at these components, and to capture concurrency and isolate which components are critical to overall performance observed. These are enclosed in an automatically constructed Response Time Service Petri Net (RT-SPN) model. A tool is provided to accept queries about work units and visualise corresponding RTSPNs. The infrastructure is also designed and implemented so as to be portable, scalable and lightweight.
10950819
An Efficient Sequential RBF Network for Gene Expression-Based Multi-category classification
This paper presents a fast and efficient sequential learning method for RBF networks that can perform classification directly for multi-category cancer diagnosis problems based on microarray data. The recently developed algorithm, referred to as Fast Growing And Pruning-RBF (FGAP-RBF) can perform incremental learning on the future data directly. No training of all the previous data is needed. This character can reduce the learning complexity and improve the learning efficiency and is greatly favored in the real implementation of a gene expression-based cancer diagnosis system. We have evaluated FGAP-RBF algorithm on a benchmark multi-category cancer diagnosis problem based on microarray data, namely GCM dataset. The results indicate that compared with the results available in literature FGAP-RBF algorithm produces a higher classification accuracy with reduced training time and implementation complexity.
43789567
A Self-Organizing Flock of Condors
6057998
Support Vector Machines-Based Quantitative Structure-Property Relationship for the Prediction of Heat Capacity
The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to construct quantitative linear and nonlinear models to compare with the results obtained by SVM. The root-mean-square (rms) errors in heat capacity predictions for the whole data set given by MLR, RBFNNs, and SVM were 4.648, 4.337, and 2.931 heat capacity units, respectively. The prediction results are in good agreement with the experimental value of heat capacity; also, the results reveal the superiority of the SVM over MLR and RBFNNs models.
11225223
Diagnosing Breast Cancer Based on Support Vector Machines
The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.