10201400
RF/sup */IPF: a weighting scheme for multimedia information retrieval
The region-based approach has become a popular research trend in the field of multimedia database retrieval. We present the Region Frequency and Inverse Picture Frequency (RF/sup */IPF) weighting, a measure developed to unify region-based multimedia retrieval systems with text-based information retrieval systems. The weighting measure gives the highest weight to regions that occur often in a small number of images in the database. These regions are considered discriminators. With this weighting measure, we can blend image retrieval techniques with TF/sup */IDF-based text retrieval techniques for large-scale Web applications. The RF/sup */IPF weighting has been implemented as a part of our experimental SIMPLIcity image retrieval system and tested on a database of about 200000 general-purpose images. Experiments have shown that this technique is effective in discriminating images of different semantics. Additionally, the overall similarity approach enables a simple querying interface for multimedia information retrieval systems.
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.
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.
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.
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.
2328843
Data-rich section extraction from HTML pages
We propose a novel algorithm, DSE (data-rich subtree extraction) to recognize and extract the data-rich section of an HTML page. We apply the DSE algorithm as a pre-processing "clean-up" step for two typical Web information retrieval problems: topic distillation and Web information extraction. Our experiments show that, for the test data sets used, the DSE algorithm can correctly identify data-rich sections of HTML pages with 100% accuracy. Therefore, it can effectively reduce the root set size for the topic distillation problem thereby improving the precision and accuracy of the IETS algorithm. Furthermore, when applied to the Web information extraction problem using the IEPAD algorithm, it can decrease the number of patterns discovered by this algorithm, thus shortening its time cost to generalize a wrapper for HTML pages.
9071197
Design and implementation of a P2P cooperative proxy cache system
In this paper, we design and implement a P2P cooperative proxy caching system based on a novel P2P cooperative proxy caching scheme. To effectively locate the cached Web documents, a TTL-based routing protocol is proposed to manage the query and response messages in the P2P cooperative proxy cache system. Furthermore, we design a predict query-route algorithm to improve the TTL-based routing protocol by adding extra information in the query message packets. Our performance studies demonstrate that the proposed message routing protocols significantly improve the performance of the P2P cooperative proxy cache system, in terms of cache hit ratio, byte hit ratio, user request latency, and the number of query messages generated in the proxy cache system, compared to the flooding based message routing protocol.
42282919
Exploring Local Community Structures in Large Networks
In this paper, we extend the concept of degree from single vertex to sub-graph, and present a formal definition of module/community in a network based on this extension. A new locally optimized algorithm is designed to find the module for a given source vertex in a network. Our analysis shows that the complexity of this algorithm is O(K2d), where K is the number of vertices to be explored in the sub-graph and d is the average degree of the vertices in the sub-graph. Based on this algorithm, we implement a JAVA tool, MoNet, for exploring local community structures in large networks. Using this tool to analyze a co-purchase network from Amazon shows that there are local community structures in this network. Further analyses on these local community structures demonstrate that media items are much easier to form compact local modules than book items do, indicating that recommending digital media items to customers based on co-purchasing information in the online store will be more efficient than recommending books.
13018290
Towards Web Mining of Query Translations for Cross-Language Information Retrieval in Digital Libraries
This paper proposes an efficient client-server-based query translation approach to allowing more feasible implementation of cross-language information retrieval (CLIR) services in digital library (DL) systems. A centralized query translation server is constructed to process the translation requests of cross-lingual queries from connected DL systems. To extract translations not covered by standard dictionaries, the server is developed based on a novel integration of dictionary resources and Web mining methods, including anchor-text and search-result methods, which exploit huge amounts of multilingual and wide-scoped Web resources as live bilingual corpora to alleviate translation difficulties, and have been proven particularly effective for extracting multilingual translation equivalents of query terms containing proper names or new terminologies. The proposed approach was implemented in a query translation engine called LiveTrans, which has been shown its feasibility in providing efficient English-Chinese CLIR services for DL.
24553
Mandarin-English Information (MEI): investigating translingual speech retrieval
Abstract This paper describes the Mandarin–English Information (MEI) project, where we investigated the problem of cross-language spoken document retrieval (CL-SDR), and developed one of the first English–Chinese CL-SDR systems. Our system accepts an entire English news story (text) as query, and retrieves relevant Chinese broadcast news stories (audio) from the document collection. Hence, this is a cross-language and cross-media retrieval task. We applied a multi-scale approach to our problem, which unifies the use of phrases, words and subwords in retrieval. The English queries are translated into Chinese by means of a dictionary-based approach, where we have integrated phrase-based translation with word-by-word translation. Untranslatable named entities are transliterated by a novel subword translation technique. The multi-scale approach can be divided into three subtasks – multi-scale query formulation, multi-scale audio indexing (by speech recognition) and multi-scale retrieval. Experimental results demonstrate that the use of phrase-based translation and subword translation gave performance gains, and multi-scale retrieval outperforms word-based retrieval.