10726926
A scalable integrated region-based image retrieval system
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. It incorporates properties of all the regions in the images by a region-matching scheme. The algorithm has been implemented as a part of our experimental SIMPLIcity (Semantics-sensitive Integrated Matching for Picture LIbraries) 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 of the original system while reducing the matching time significantly.
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.
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.
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.
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.
579393
Chinese input with keyboard and eye-tracking: an anatomical study
Chinese input presents unique challenges to the field of human computer interaction. This study provides an anatomical analysis of today's standard Chinese input process, which is based on pinyin, a phonetic spelling system in Roman characters. Through a combination of human performance modeling and experimentation, our study decomposed the Chinese input process into sub-tasks and found that choice reaction time and numeric keying, two component resulted from the large number of homophones in Chinese, were the major usability bottlenecks. Choice reaction alone took 36% of the total input time in our experiment. Numeric keying for multiple candidates selection tends to take the user's attention away from the computer visual screen. We designed and implemented the EASE (Eye Assisted Selection and Entry) system to help maintaining complete touch-typing experience without diverting visual (spacebar) and implicit eye-tracking to replace the numeric keystrokes. Our experiment showed that such a system could indeed work, even with today's imperfecteye-tracking technology.
17168623
Traffic classification for application specific peering
Peer-to-Peer (P2P) applications [4], [6], [2], [3] have increased traffic significantly. We propose application specific peering that allows searches for a resource (using existing or modified P2P protocols) to be directed to a copy of resource on an ISP's network, using a novel technique that automatically classifies traffic (shown in Fig 1) as belonging to an ISP, its customers, peers, or their customers. We examined a large amount of traffic belonging to three popular P2P protocols and used a graph transformation to detect connected components. We partition traffic in each component into signaling and data sections based on request/response sizes of P2P protocols and extract high traffic volume (by bytes) entities by examining large connected components in the graph. We discuss the important components of our traffic classification system here. We analyzed router-level data from multiple Internet Gateway Routers (IGRs) across AT&T IP backbone using Cisco's NetFlow services. Note that statistics are from a subset of IGRs at some AT&T's peering links. They are not representative of overall traffic pattem on AT&T IP backbone. We examined DirectConnect[2], Gnutella[3], and FastTrack[4], extracting records that matched the default application ports (source or destina• tion, 411/412, 6346/6347, and 1214 (FastTrack) respectively), involving TCP traffic. Data was gathered over three separate months in 2001, each lasting 5-7 days. The number of netflow records varies from a low of 0.5M (DirectConnect) to a high
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.
2366018
Constrained texture synthesis by scalable sub-patch algorithm
In this paper, we propose a novel scalable sub-patch algorithm for texture synthesis. Texture synthesis is an important research topic in computer graphics and image processing. We present an efficient algorithm which can paste a patch each time without boundary processing technique. In particular, the proposed sub-patch algorithm can be used in constrained texture synthesis to fill the large lost region. On the other hand, the searching neighborhood pixels can be changed due to different patch size in image reconstruction. The comparison of previous algorithms is also provided in this paper. We present a first algorithm used in constrained texture synthesis by regular synthesizing order
207156320
Livenotes: a system for cooperative and augmented note-taking in lectures
We describe Livenotes, a shared whiteboard system and educational practice that uses wireless communication and tablet computing to support real-time conversations within small groups of students during lectures, independent of class size. We present an interface design that enables group members to interact with one another by taking lecture notes cooperatively, as well as to augment student note-taking by providing instructor slides in the background to annotate over. Livenotes was designed to facilitate more efficient, stimulating modes of learning that other collaborative approaches do not. We report how the system impacts cooperative learning in an undergraduate class and how students interacted with background slides in the workspace. We conclude with directions for improving the system and learning practice.