Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the p...
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ISBN:
(纸本)9781595937025
Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspective. In particular, we modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This graph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent Isoperimetric High-order Co-clustering(CIHC) framework. Although a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in Web image clustering and scalability to large image repositories that CIHC is able to achieve. We demonstrate this through experimental results performed on real Web images. Copyright 2007 ACM.
Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factor...
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ISBN:
(纸本)9781595937025
Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively. Copyright 2007 ACM.
In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate...
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This paper is about the optimization of beamforming weights in the nonlinear array antenna system. We consider the multi-objective optimization using the evolutionary algorithm. First, we constrain two variables - the...
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ISBN:
(纸本)0780385217
This paper is about the optimization of beamforming weights in the nonlinear array antenna system. We consider the multi-objective optimization using the evolutionary algorithm. First, we constrain two variables - the width of the main-lobe, the level of the side-lobe. Second, we constrain an additional variable, the energy of beamforming weights, on the former system. In each case, we compute the costs of beamforming weights in each generation. At the end of generations, we can get the Pareto frontier which means the optimal solution set. To demonstrate the performance of the proposed optimization, we compare the results between the proposed algorithm and the conventional window method.
The ability to detect shadows Is a critical feature of any Intelligent transportation system (ITS). The Improper handling of shadows can be the cause of erroneous conclusions in traffic analysis. Because vision-based ...
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The goal of this project is to develop a passive vision-based sensing system capable of monitoring an intersection by observing the vehicle and pedestrian flow, and predicting situations that might give rise to accide...
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The goal of this project is to develop a passive vision-based sensing system capable of monitoring an intersection by observing the vehicle and pedestrian flow, and predicting situations that might give rise to accidents. A single camera mounted at an arbitrary position looking at an intersection is used. However, for extended applications multiple cameras will be needed. Some of the key elements are camera calibration, motion tracking, vehicle classification, and predicting collisions. In this paper, we focus on motion tracking. Motion segmentation is performed using an adaptive background model that models each pixel as a mixture of Gaussians. The method used is similar to the Stauffer method for motion segmentation. Tracking of objects is performed by computing the overlap between oriented bounding boxes. The oriented boxes are computed by vector quantization of blobs in the scene. The principal angles computed during vector quantization along with other cues of the object are used for classification of detected entities into vehicles and pedestrians.
We present the adaptive manifold self-organising map (AMSOM) for a face retrieval system. Our experimental results show that it has an excellent potential for face retrieval applications. As compared to the more tradi...
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ISBN:
(纸本)9628576623
We present the adaptive manifold self-organising map (AMSOM) for a face retrieval system. Our experimental results show that it has an excellent potential for face retrieval applications. As compared to the more traditional sub-space self-organising map, the results in many cases are better.
Accurate and automatic assessment of angiograms has been sought as a powerful diagnostic tool in medical analysis, such as in diabetic retinopathy. These retinal angiogram images are characterized by poor local contra...
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Accurate and automatic assessment of angiograms has been sought as a powerful diagnostic tool in medical analysis, such as in diabetic retinopathy. These retinal angiogram images are characterized by poor local contrast. Applications of existing edge detection algorithms yield unsatisfactory results. In this paper, a set of cascaded linear directional filters is used to better enhance edges. Results show improvement in visual quality of the images.
This paper proposes a framework with essential components and processes for object-based image retrieval based on semantically meaningful classes of objects in images. An instantiation of the framework is presented to...
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