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Web navigation support by means of proximity-driven assistant agents

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY 2006年第4期57卷 515-527页

作者： Loia, V Pedrycz, W Senatore, S Sessa, MI Univ Salerno Dipartimento Matemat & Informat I-84084 Fisciano SA Italy Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent-based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar-pages searching mode. Our agent-based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity-oriented fuzzy clustering, and knowledge-based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.

关键词： navigation user aids query by example user profiles fuzzy logic

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A probabilistic framework for semantic video indexing, filtering, and retrieval

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IEEE TRANSACTIONS ON MULTIMEDIA 2001年第1期3卷 141-151页

作者： Naphade, MR Huang, TS Univ Illinois Dept Elect & Comp Engn Beckman Inst Adv Sci & Technol Urbana IL 61801 USA

Semantic filtering and retrieval of multimedia content is crucial for efficient use of the multimedia data repositories., Video query by semantic keywords is one of the most difficult problems in multimedia data retrieval. The difficulty lies in the mapping between low-level video representation and high-level semantics. We therefore formulate the multimedia content access problem as a multimedia pattern recognition problem. We propose a probabilistic framework for semantic video indexing, which can support filtering and retrieval and facilitate efficient content-based. access. To map low-level features to high-level semantics we propose probabilistic multimedia objects (multijects). examples of multijects;in movies include explosion, mountain, beach, outdoor, music etc. Semantic concepts in videos interact and to model this interaction explicitly, we propose a network of multijects (multinet). Using probabilistic models for six site multijects, rocks, sky, snow, water-body, forestry/greenery and outdoor and using a Bayesian belief network as the multinet we demonstrate the application of this: framework to semantic indexing. We demonstrate how detection performance can be significantly improved using the multinet to take interconceptual relationships into account. We also show how the multinet can fuse heterogeneous features to support detection based on inference and reasoning.

关键词： Bayesian belief networks hidden Markov models likelihood ratio test multimedia understanding probabilistic graphical networks ROC curves semantic video indexing query by example query by keywords semantic video indexing

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querying Knowledge Graphs by example Entity Tuples

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2015年第10期27卷 2797-2811页

作者： Jayaram, Nandish Khan, Arijit Li, Chengkai Yan, Xifeng Elmasri, Ramez Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76010 USA Swiss Fed Inst Technol Syst Grp Zurich Switzerland Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA

We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content-rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over "simple" tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, Graph query by example (GQBE), automatically discovers a weighted hidden maximum query graph based on input query tuples, to capture a user's query intent. It then efficiently finds and ranks the top approximate matching answer graphs and answer tuples. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.

关键词： Knowledge graphs entity graphs query by example graph query processing

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Low Resource Keyword Search With Synthesized Crosslingual Exemplars

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2019年第7期27卷 1126-1135页

作者： Yusuf, Bolaji Gundogdu, Batuhan Saraclar, Murat Bogazici Univ Dept Elect & Elect Engn TR-34342 Istanbul Turkey Natl Def Univ Naval Acad Dept Elect & Elect Engn TR-34342 Istanbul Turkey

The transfer of acoustic data across languages has been shown to improve keyword search (KWS) performance in data-scarce settings. In this paper, we propose a way of performing this transfer that reduces the impact of the prevalence of out-of-vocabulary (OOV) terms on KWS in such a setting. We investigate a novel usage of multilingual features for KWS with very little training data in the target languages. The crux of our approach is the use of synthetic phone exemplars to convert the search into a query-by-example task, which we solve with the dynamic time warping algorithm. Using bottleneck features obtained from a network trained multilingually on a set of (source) languages, we train an extended distance metric learner (EDML) for four target languages from the IARPA Babel program (which are distinct from the source languages). Compared with a baseline system that is based on automatic speech recognition (ASR) with a multilingual acoustic model, we observe an average term weighted value improvement of 0.0603 absolute (74% relative) in a setting with only 1 h of training data in the target language. When the data scarcity is relaxed to 10 h, we find that phone posteriors obtained by fine-tuning the multilingual network give better EDML systems. In this relaxed setting, the EDML systems still perform better than the baseline on OOV terms. Given their complementary natures, combining the EDML and the ASR-based baseline results in even further performance improvements in all settings.

关键词： Spoken term detection distance metric learning exemplar matching dynamic time warping multilingual features query by example low resource keyword search transfer learning

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New spatial-organization-based scale and rotation invariant features for heterogeneous-content camera-based document image retrieval

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PATTERN RECOGNITION LETTERS 2018年第Sep.1期112卷 153-160页

作者： Quoc Bao Dang Coustaty, Mickael Luqman, Muhammad Muzzamil Ogier, Jean Marc Cao De Tran Univ La Rochelle Lab L3i Ave Michel Crpeau F-17042 La Rochelle 1 France Can Tho Univ Coll Informat & Commun Technol 3-2 St Can Tho City Vietnam

In this paper, we extend our earlier proposed feature descriptor named Scale and Rotation Invariant Features (SRIF) and a camera-based heterogeneous-content information spotting system based on the latter. Through its capacity to manage heterogeneous content in document images, SRIF represents an extension to existing strategies such as LLAH, which are dedicated to textual document images. This paper proposes new extensions of SRIF based on geometrical constraints between pairs of nearest points around a key-point. SRIF has built-in capabilities to deal with feature point extraction errors which are introduced in camera-captured documents. To validate our method and compare it to the state-of-the-art, we have constructed three datasets of heterogeneous-content document images, along with the corresponding ground truths. Our experiment results confirm that SRIF outperforms the state-of-the-art in terms of processing time with equal or greater recall and precision for retrieval and spotting results. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Document capture Feature extraction Feature descriptor Feature evaluation Feature selection Document indexing Information search and retrieval Camera-based document analysis and recognition Document image retrieval query by example Pattern recognition

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QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases

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INFORMATION SCIENCES 2014年 264卷 211-229页

作者： Cordeiro, Robson L. F. Guo, Fan Haverkamp, Donna S. Horne, James H. Hughes, Ellen K. Kim, Gunhee Romani, Luciana A. S. Coltri, Priscila P. Souza, Tamires T. Traina, Agma J. M. Traina, Caetano, Jr. Faloutsos, Christos Univ Sao Paulo BR-13560970 Sao Carlos SP Brazil Carnegie Mellon Univ Pittsburgh PA 15213 USA Sci Applicat Int Corp Mclean VA 22102 USA Embrapa Agr Informat BR-13083886 Campinas SP Brazil Univ Estadual Campinas BR-13083970 Campinas SP Brazil

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user's attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) - given an image set, very few images have labels, find the most appropriate labels for the rest;and (ii) Mining and attention routing - in the same setting, find clusters, the top-N-O outlier images, and the N-R images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method's practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Low-labor labeling Summarization Outlier detection query by example Clustering Satellite imagery

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Graphical search for images by PictureFinder

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MULTIMEDIA TOOLS AND APPLICATIONS 2005年第2期27卷 229-250页

作者： Hermes, T Miene, A Herzog, O Univ Bremen TZI Ctr Comp Technol Digital Media Image Proc D-28359 Bremen Germany

The text searching paradigm still prevails even when users are looking for image data for example in the Internet. Searching for images mostly means searching on basis of annotations that have been made manually. When annotations are left empty, which is usually the case, searches on image file names are performed. This may lead to surprising retrieval results. The graphical search paradigm, searching image data by querying graphically, either with an image or with a sketch, currently seems not to be the preferred method partly because of the complexity in designing the query. In this paper we present our PictureFinder system, which currently supports "full image retrieval" in analogy to full text retrieval. PictureFinder allows graphical queries for the image the user has in his mind by sketching colored and/or textured regions or by whole images (query by example). By adjusting the search tolerances for each region and image feature (i.e. hue, saturation, lightness, texture pattern and coverage) the user can tune his query either to find images matching his sketch or images which differing from the specified colors and/or textures to a certain degree. To compare colors we propose a color distance measure that takes into account the fact that different colors spread differently in the color space, and which take into account that the position of a region in an image may be important. Furthermore, we show our query by example approach. Based on the example image chosen by the user, a graphical query is generated automatically and presented to the user. One major advantage of this approach is the possibility to change and adjust a query by example in the same way as a query which was sketched by the user. By deleting unimportant regions and by adjusting the tolerances of the remaining regions the user may focus on image details which are important to him.

关键词： image retrieval color matching similarity search query by example

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Boosting the handwritten word spotting experience by including the user in the loop

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PATTERN RECOGNITION 2014年第3期47卷 1063-1072页

作者： Rusinol, Marcal Llados, Josep Univ Autonoma Barcelona Comp Vis Ctr Dept Ciencies Computacio E-08193 Bellaterra Barcelona Spain

In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Handwritten word spotting query by example Relevance feedback query fusion Multidimensional scaling

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PICquery - A HIGH-LEVEL query LANGUAGE FOR PICTORIAL DATABASE-MANAGEMENT

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1988年第5期14卷 630-638页

作者： JOSEPH, T CARDENAS, AF UNIV CALIF LOS ANGELES DEPT COMP SCILOS ANGELESCA 90024

A reasonably comprehensive set of data accessing and manipulation operations that should be supported by a generalized pictorial database management system (PDBMS) is proposed. A corresponding high-level query language, PICquery, is presented and illustrated through examples. PICquery has been designed with a flavor similar to QBE as the highly nonprocedural and conservational language for the pictorial database management system PICDMS. PICquery and a relational QBE-like language would form the language by which a user could access conventional relational databases and at the same time pictorial databases managed by PICDMS or other robust PDBMS. This language interface is part of an architecture aimed toward data heterogeneity transparency over pictorial and nonpictorial databases.

关键词： database management MANAGEMENT SYSTEMS query by example data base language visual databases Language

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example-Based Retrieval of Alike Land-Cover Scenes From NLCD2006 Database

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2013年第1期10卷 155-159页

作者： Jasiewicz, Jaroslaw Stepinski, Tomasz F. Adam Mickiewicz Univ Inst Geoecol & Geoinformat PL-60680 Poznan Poland Univ Cincinnati Dept Geog Cincinnati OH 45221 USA

query-by-image-content (QBIC) tools are in demand in geospatial community because they enable exploration and mining of the rapidly increasing database of remotely sensed images. Accompanying the growth of the imagery database is the increase in the number of image-derived products, such as high-resolution large-spatial-extent maps of land cover/land use (LCLU). QBIC-like tools for exploration and mining of such products would significantly enhance their value. In this letter, we present a method for retrieval of alike scenes from a category-valued geospatial database of which an LCLU map is a particular example. Alikeness between the two scenes is tantamount to similarity between their spatial patterns of class labels. Our method works on the principle of query by example, its input is a reference scene, and its output is a similarity map indicating a degree of alikeness between a location on the map and the reference. The two core components of the method are as follows: scene signature-an encapsulation of the scene pattern by means of probability distribution of class labels and the sizes of the patches that they form, and scene similarity-a mutual-information-based function that assigns a level of similarity between any two scenes based on their signatures. The method is described in detail and applied to the National Land Cover Dataset 2006. Two examples of queries on this data set are presented and discussed. The applicability of the method to other data sets is discussed.

关键词： Content-based image retrieval land-cover data set query by example similarity

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