13790019
Scalable integrated region-based image retrieval using IRM and statistical clustering
Statistical clustering is critical in designing scalable image retriev al systems. In this paper, we present a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images that incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based on individual regions, our overall similarity approach (a) reduces the influence of inaccurate segmentation, (b) helps to clarify the semantics of a particular region, and (c) enables a simple querying interface for region-based image retrieval systems. The algorithm has been implemented as a part of our experimental SIMPLIcity image retrieval system and tested on large-scale image databases of both general-purpose images and pathology slides. Experiments have demonstrated that this technique maintains the accuracy and robustness of the original system while reducing the matching time significantly.
14843520
Deriving knowledge from figures for digital libraries
Figures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document retrieval. We present our system on automatic categorization of figures and extraction of data from 2-D plots. A machine-learning based method is used to categorize figures into a set of predefined types based on image features. An automated algorithm is designed to extract data values from solid line curves in 2-D plots. The semantic type of figures and extracted data values from 2-D plots can be integrated with textual information within documents to provide more effective document retrieval services for digital library users. Experimental evaluation has demonstrated that our system can produce results suitable for real-world use.
735304
PARAgrab: a comprehensive architecture for web image management and multimodal querying
We demonstrate PARAgrab - a scalable Web image archival, retrieval, and annotation system that supports multiple querying modalities. The underlying architecture of our large-scale Web image database is described. Querying and visualization techniques used in the system are explained.
1791876
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions, each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.
7844670
A sparse support vector machine approach to region-based image categorization
Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.
4227250
Content-based image retrieval by clustering
In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This is known as the semantic gap. We introduce a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which tackles the semantic gap problem based on a hypothesis: semantically similar images tend to be clustered in some feature space. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60, 000 images from COREL demonstrate improved performance.
2749844
Image and Video Segmentation by Anisotropic Kernel Mean Shift
Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. In this paper we present an anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the local structure of the image or video. We decompose the anisotropic kernel to provide handles for modifying the segmentation based on simple heuristics. Experimental results show that the anisotropic kernel mean shift outperforms the original mean shift on image and video segmentation in the following aspects: 1) it gets better results on general images and video in a smoothness sense; 2) the segmented results are more consistent with human visual saliency; 3) the algorithm is robust to initial parameters.
4374799
Appearance manifolds for modeling time-variant appearance of materials
We present a visual simulation technique called appearance manifolds for modeling the time-variant surface appearance of a material from data captured at a single instant in time. In modeling time-variant appearance, our method takes advantage of the key observation that concurrent variations in appearance over a surface represent different degrees of weathering. By reorganizing these various appearances in a manner that reveals their relative order with respect to weathering degree, our method infers spatial and temporal appearance properties of the material's weathering process that can be used to convincingly generate its weathered appearance at different points in time. Results with natural non-linear reflectance variations are demonstrated in applications such as visual simulation of weathering on 3D models, increasing and decreasing the weathering of real objects, and material transfer with weathering effects.
15308504
Efficient and accurate Ethernet simulation
The Internet is increasingly being called upon to provide different levels of service to different applications and users. A practical problem in doing so is that although Ethernet is one of the hops for nearby all communication in the Internet, it does not provide any QoS guarantees. A natural question, therefore, is the effect of offered load on Ethernet throughput and delay. In this paper, we present several techniques for accurately and efficiently modeling the behavior of a heavily loaded Ethernet link. We propose an efficient distributed simulation model, called Fast Ethernet Simulation, that empirically models an Ethernet link to quickly and accurately simulate it. By eliminating the implementation of the CSMA/CD protocol, our approach reduces computational complexity drastically while still maintaining desirable accuracy. Performance results show that our techniques not only add very little overhead (less than 5% in our tests) to the basic cost of simulating an Ethernet link, but also closely match real-world measurements. We also present efficient techniques for compressing cumulative distributions using hyperbolic curves and for monitoring the load on a heavily loaded link. Finally, we show applications to illustrate the potential usage of the Fast Ethernet Simulation.
3191547
A measurement study of Internet bottlenecks
Recent advances in Internet measurement tools have made it possible to locate bottleneck links that constrain the available bandwidth of Internet paths. In this paper, we provide a detailed study of Internet path bottlenecks. We focus on the following four aspects: the persistence of bottleneck location, the sharing of bottlenecks among destination clusters, the packet loss and queueing delay of bottleneck links, and the relationship with router and link properties, including router CPU load, router memory load, link traffic load, and link capacity. We find that 20% - 30% of the source-destination pairs in our measurement have a persistent bottleneck; fewer than 10% of the destinations in a prefix cluster share a bottleneck more than half of the time; 60% of the bottlenecks on lossy paths can be correlated with a loss point no more than 2 hops away; and bottlenecks can be clearly correlated with link load, while presenting no strong relationship with link capacity, router CPU and memory load.
37176365
A content-based scheme for CT lung image retrieval
A content based scheme to retrieve computed tomographic images (CT) of the lung is presented. It consists of a visual based user interface to allow the query be made by line drawing the interested (abnormal) regions; and a training scheme to classify the relationship between the images stored in database. The system will output a set of candidate images that are texturally similar to the query image. We marked the abnormal portions of each training image by a polygonal or rectangular shape manually because it requires expert knowledge. Then, the texture features of each marked region are extracted on the DCT or SADCT transform domain. In the training stage, the extracted DCT/SADCT coefficients are fed into a Kohonen self organizing network to find the relationship for classification. In the query stage, the system first checks which texture category the query image is in, then uses some geometrical characteristics to identify the most likely candidate image. Experimental results show that 96% of the queries can be correctly retrieved where the original image is in the candidate set.