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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 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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Subgraph spotting in graph representations of comic book images

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PATTERN RECOGNITION LETTERS 2018年 112卷 118-124页

作者： Thanh Nam Le Luqman, Muhammad Muzzamil Dutta, Anjan Heroux, Pierre Rigaud, Christophe Guerin, Clement Foggia, Pasquale Burie, Jean-Christophe Ogier, Jean-Marc Llados, Josep Adam, Sebastien Univ La Rochelle L3i La Rochelle France Univ Salerno Salerno Italy Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain Univ Rouen LITIS Rouen France

Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Attributed graph Region adjacency graph Graph matching Graph isomorphism Subgraph isomorphism Subgraph spotting Graph indexing Graph retrieval query by example Dataset and comic book images

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Event retrieval in video archives using rough set theory and partially supervised learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2012年第1期57卷 145-173页

作者： Shirahama, Kimiaki Matsuoka, Yuta Uehara, Kuniaki Kobe Univ Grad Sch Econ Nada Ku Kobe Hyogo 6578501 Japan Kobe Univ Grad Sch Engn Nada Ku Kobe Hyogo 6578501 Japan Kobe Univ Grad Sch Syst Informat Nada Ku Kobe Hyogo 6578501 Japan

This paper develops a query-by-example method for retrieving shots of an event (event shots) using example shots provided by a user. The following three problems are mainly addressed. Firstly, event shots cannot be retrieved using a single model as they contain significantly different features due to varied camera techniques, settings and so forth. This is overcome by using rough set theory to extract multiple classification rules with each rule specialized to retrieve a portion of event shots. Secondly, since a user can only provide a small number of example shots, the amount of event shots retrieved by extracted rules is inevitably limited. We thus incorporate bagging and the random subspace method. Classifiers characterize significantly different event shots depending on example shots and feature dimensions. However, this can result in the potential retrieval of many unnecessary shots. Rough set theory is used to combine classifiers into rules which provide greater retrieval accuracy. Lastly, counter example shots, which are a necessity for rough set theory, are not provided by the user. Hence, a partially supervised learning method is used to collect these from shots other than example shots. Counter example shots, which are as similar to example shots as possible, are collected because they are useful for characterizing the boundary between event shots and the remaining shots. The proposed method is tested on TRECVID 2009 video data.

关键词： query by example Rough set theory Bagging Random subspace Small sample size problem Partially supervised learning

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querying knowledge graphs through positive and negative examples and feedback

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2024年第5期62卷 1165-1186页

作者： Akritidis, Akritas Tzitzikas, Yannis Fdn Res & Technol Hellas FORTH Inst Comp Sci N Plastira 100 Iraklion Greece Univ Crete Dept Comp Sci Iraklion Crete Greece

The formulation of structured queries over Knowledge Graphs is not an easy task. To alleviate this problem, we propose a novel interactive method for SPARQL query formulation, for enabling users (plain and advanced) to formulate gradually queries by providing examples and various kinds of positive and negative feedback, in a manner that does not pre-suppose knowledge of the query language or the contents of the Knowledge Graph. In comparison to other example-based query approaches, distinctive features of our approach is the support of negative examples, and the positive/negative feedback on the generated constraints. We detail the algorithmic aspect and we present an interactive user interface that implements the approach. The application of the model on real datasets from DBpedia (Movies, Actors) and other datasets (scientific papers), showcases the feasibility and the effectiveness of the approach. A task-based evaluation that included users that are not familiar with SPARQL, provided positive evidence that the interaction is easy-to-grasp and enabled most users to formulate the desired queries.

关键词： query by example Knowledge graphs

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INACT-INDECT Advanced Image Cataloguing Tool

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MULTIMEDIA TOOLS AND APPLICATIONS 2014年第1期68卷 95-110页

作者： Grega, Michal Bryk, Damian Napora, Maciej AGH Univ Sci & Technol PL-30059 Krakow Poland

Child pornography possession and distribution are crimes which are prosecuted in most countries around the world. In some cases the law is so strict that even police forces are not allowed to gather and catalogue evidence for future reference. This paper presents an innovative solution to this problem. The authors present tools for cataloguing high- and low-level metadata of the evidence material. Furthermore, a tool for fast and accurate search for such evidence in suspects' file systems is proposed.

关键词： Live forensics query by example Bees Algorithm Child pornography

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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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High-level feature aggregation for fine-grained architectural floor plan retrieval

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IET COMPUTER VISION 2018年第5期12卷 702-709页

作者： Sharma, Divya Chattopadhyay, Chiranjoy Indian Inst Technol Jodhpur Dept Comp Sci & Engn Jodhpur Rajasthan India

Due to the massive growth of real estate industry, there is an increase in the number of online platforms designed for finding homes/furnished properties. Instead of descriptive words, query by example is always a preferred method for retrieval. Floor plans are the basic 2D representation giving an idea about the building structure at a particular level. The authors propose a framework for the retrieval of similar architectural floor plans under the query by example paradigm. They propose a novel algorithm to extract high-level semantic features from an architectural floor plan. Fine-grained retrieval using weighted sum of the features is proposed, where a feature can be given more preference over others, during retrieval. Experiments were performed on publicly available dataset containing 510 floor plans and compared with existing state-of-the-art techniques. Their proposed method outperforms others both in qualitative and quantitative terms.

关键词： architecture feature extraction floors image retrieval construction industry image representation fine-grained architectural floor plan retrieval estate industry online platforms homes furnished properties high-level semantic features fine-grained retrieval 2D representation architectural floor plans high-level feature aggregation query by example weighted sum

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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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Scalable color image indexing and retrieval using vector wavelets

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2001年第5期13卷 851-861页

作者： Albuz, E Kocalar, E Khokhar, AA Sony Elect San Jose CA 95134 USA UltimateTV Inc Sunnyvale CA 94086 USA Univ Illinois Dept Elect & Comp Sci Chicago IL 60607 USA

This paper presents a scalable content-based image indexing and retrieval system based on vector wavelet coefficients of color images. Highly decorrelated wavelet coefficient planes are used to acquire a search efficient feature space. The feature space is subsequently indexed using properties of all the images in the database. Therefore, the feature key of an image not only corresponds to the content of the image itself but also to how much the image is different from the other images being stored in the database. The search time linearly depends on the number of images similar to the query image and is independent of the database size. We show that, in a database of 5,000 images, query search takes less than 30 msec on a 266 MHz Pentium II processor, compared to several seconds of retrieval time in the earlier systems proposed in the literature.

关键词： wavelet transform content-based image indexing query by example image retrieval scalable indexing retrieval systems

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