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query by example: Semantic Traffic Scene Retrieval Using LLM-Based Scene Graph Representation

SENSORS 2025年第8期25卷 2546-2546页

作者： Tian, Yafu Carballo, Alexander Li, Ruifeng Thompson, Simon Takeda, Kazuya Nagoya Univ Grad Sch Informat Furo ChoChikusa Ku Nagoya 4648603 Japan Harbin Inst Technol State Key Lab Robot & Intelligent Syst Harbin 150000 Peoples R China Nagoya Univ Tier IV Inc Nakamura Ward Open Innovat Ctr 1-3 Mei Eki 1 Chome Nagoya 4506610 Japan Gifu Univ Fac Engn 1-1 Yanagido Gifu 5011193 Japan Nagoya Univ Inst Innovat Future Soc Furo ChoChikusa Ku Nagoya 4648601 Japan

In autonomous driving, retrieving a specific traffic scene in huge datasets is a significant challenge. Traditional scene retrieval methods struggle to cope with the semantic complexity and heterogeneity of traffic scenes and are unable to meet the variable needs of different users. This paper proposes "query-by-example", a traffic scene retrieval approach based on Visual-Large Language Model (VLM)-generated Road Scene Graph (RSG) representation. Our method uses VLMs to generate structured scene graphs from video data, capturing high-level semantic attributes and detailed object relationships in traffic scenes. We introduce an extensible set of scene attributes and a graph-based scene description to quantify scene similarity. We also propose a RSG-LLM benchmark dataset containing 1000 traffic scenes, their corresponding natural language descriptions, and RSGs to evaluate the performance of LLMs in generating RSGs. Experiments show that our method can effectively retrieve semantically similar traffic scenes from large databases, supporting various query formats, including natural language, images, video clips, rosbag, etc. Our method provides a comprehensive and flexible framework for traffic scene retrieval, promoting its application in autonomous driving systems.

关键词： traffic scene retrieval visual LLMs subgraph isomorphism matching scene graph query by example

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query by example IN EARTH-OBSERVATION IMAGE ARCHIVE USING DATA COMPRESSION-BASED APPROACH

QUERY BY EXAMPLE IN EARTH-OBSERVATION IMAGE ARCHIVE USING DA...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Espinoza-Molina, D. Quartulli, M. Datcu, M. German Aerosp Ctr Remote Sensing Technol Inst D-82234 Wessling Germany

ISBN: (纸本)9781467311595

This paper presents an implementation of query by example in Earth Observation image archive using data compression-based approach. Data compression approach allows to exploit the compression properties of the objects and to estimate the shared information between them, this concept is extended to image retrieval for finding similar objects in the image archive. Our implementation is based on LZW algorithm for compressing the image content and extracting features of the images. The fast compression distance (FCD) is defined as a similarity metric in order to retrieve the most similar images. This tool is satisfactory implemented and tested using optical and SAR images.

关键词： Content based image retrieval High resolution optical and SAR similarity metrics fast compression distance image retrieval query by example

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Phonetic subspace features for improved query by example spoken term detection

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SPEECH COMMUNICATION 2018年 103卷 27-36页

作者： Ram, Dhananjay Asaei, Afsaneh Bourlard, Herve Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

This paper addresses the problem of detecting speech utterances from a large audio archive using a simple spoken query, hence referring to this problem as "query by example Spoken Term Detection" (QbE-STD). This still open pattern matching problem has been addressed in different contexts, often based on variants of the Dynamic Time Warping (DTW) algorithm. In the work reported here, we exploit Deep Neural Networks (DNN) and the so inferred phone posteriors to better model the phonetic subspaces and, consequently, improve the QbE-STD performance. Those phone posteriors have indeed been shown to properly model the union of the underlying low-dimensional phonetic subspaces. Exploiting this property. we investigate here two methods relying on sparse modeling and linguistic knowledge of sub-phonetic components. Sparse modeling characterizes the phonetic subspaces through a dictionary for sparse coding. Projection of the phone posteriors through reconstruction on the corresponding subspaces using their sparse representation enhance those phone posteriors. On the other hand, linguistic knowledge driven sub-phonetic structures are identified using phonological posteriors which consists of the probabilities of phone attributes estimated by DNNs, resulting in a new set of feature vectors. These phonological posteriors provide complementary information and a distance fusion method is proposed to integrate information from phone and phonological posterior features. Both posterior features are used for query detection using DTW and evaluated on AMI database. We demonstrate that the subspace enhanced phone posteriors obtained using sparse reconstruction outperforms the conventional DNN posteriors. The distance fusion technique gives further improvement in QbE-STD performance. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Deep neural network Phone posterior Phonological posterior Sparse representation Dictionary learning query by example Spoken term detection

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Sparse Subspace Modeling for query by example Spoken Term Detection

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第6期26卷 1126-1139页

作者： Ram, Dhananjay Asaei, Afsaneh Bourlard, Herve Idiap Res Inst CH-1920 Martigny Switzerland Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland

This paper focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. Current state-of-the-art approaches to tackle this problem rely on dynamic programming based template matching techniques using phone posterior features extracted at the output of a deep neural network. Previously, it has been shown that the space of phone posteriors is highly structured, as a union of low-dimensional sub-spaces. To exploit the temporal and sparse structure of the speech data, we investigate here three different QbE-STD systems based on sparse model recovery. More specifically, we use query examples to model the query subspace using dictionary for sparse coding. Reconstruction errors calculated using sparse representation of feature vectors are then used to characterize the underlying subspaces. The first approach uses these reconstruction errors in a dynamic programming framework to detect the spoken query, resulting in a much faster search compared to standard template matching. The other two methods aim at merging template matching and sparsity-based approaches to further improve the performance. The first one proposes to regularize the template matching local distances using sparse reconstruction errors. The second approach aims at using the sparse reconstruction errors to rescore (improve) the template matching likelihood. Experiments on two different databases (AMI and MediaEval) show that the proposed hybrid systems perform better than a highly competitive QbE-STD baseline system.

关键词： Speech processing spoken term detection query by example deep neural network posterior probabilities sparse recovery modeling sparse representation subspace detection subspace regularization

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Neural Network Based End-to-End query by example Spoken Term Detection

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 1416-1427页

作者： Ram, Dhananjay Miculicich, Lesly Bourlard, Herve Idiap Res Inst CH-1920 Martigny Switzerland Idiap Res Inst CH-1920 Martigny Switzerland Ecole Polytech Fed Lausanne EPFL CH-1015 Lausanne Switzerland

This article focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. State-of-the-art approaches primarily rely on dynamic time warping (DTW) based template matching techniques using phone posterior or bottleneck features extracted from a deep neural network (DNN). We use both monolingual and multilingual bottleneck features, and show that multilingual features perform increasingly better with more training languages. Previously, it has been shown that the DTW based matching can be replaced with a CNN based matching while using posterior features. Here, we show that the CNN based matching outperforms DTW based matching using bottleneck features as well. In this case, the feature extraction and pattern matching stages of our QbE-STD system are optimized independently of each other. We propose to integrate these two stages in a fully neural network based end-to-end learning framework to enable joint optimization of those two stages simultaneously. The proposed approaches are evaluated on two challenging multilingual datasets: Spoken Web Search 2013 and query by example Search on Speech Task 2014, demonstrating in each case significant improvements.

关键词： Feature extraction Task analysis Neural networks Training Hidden Markov models Neurons Speech processing Spoken term detection query by example deep neural network bottleneck features end-to-end subsequence detection

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Content-based image retrieval: a comparison between query by example and image browsing map approaches

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JOURNAL OF INFORMATION SCIENCE 2004年第3期30卷 254-267页

作者： Yang, CC Chinese Univ Hong Kong Dept Syst Engn & Engn Management Shatin Hong Kong Peoples R China

Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content-based image retrieval is a promising approach because of its automatic indexing and retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation and feature dissimilarity function. However, users have difficulties in representing their information needs in queries to content-based image retrieval systems. In this paper, we investigate two approaches, query by example and image browsing map. Activities to support the information seeking behavior are analyzed. The performance of these approaches is measured by a user evaluation. It is found that the image browsing map provides more functionalities and capabilities to support the features of information seeking behavior and produces better performance in searching images.

关键词： digital images content based image retrieval search strategies query by example image browsing maps information seeking behaviour user evaluation retrieval performance

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4th Internet Imaging Conference

作者： Da Rugna, J Konik, H Univ St Etienne LIGIV F-42000 St Etienne France

ISBN: (纸本)0819448184

query by example is a common model developed for content-based image retrieval. The purpose of such a tool is to extract from a large database the most similar images to a request one. In practice, the meaningful characteristics of each image are first extracted. Then, each region is described with a vector composed with classical statistical features or spatial relationships. Finally, the system proposes to the user the images that minimize a certain similarity distance computed on each vector. Nevertheless, query by example depends on a criterion determined by the user. Objectively, this last step of any content-based retrieval system then suffers from a large difficulty to express the real hope of the user. Thus, the results are always constrained to the similarity distance definition. In actual fact, it is riot sufficient to compute good descriptors, a robust and adequate distance to compare them is also necessary. Our purpose is more precisely to evaluate different similarity "blob-to-blob" distances. In fact, each image is first described locally using a coarse segmentation and the meaningful regions are extracted using a selection process based on color homogeneity. Among all these parameters, different distances are discussed using different approaches: spatial, shape, color and texture similarities.

关键词： content-based retrieval systems query by example similarity distances descriptors evaluation

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Reduction of Speech Data Posteriorgrams by Compressing Maximum-likelihood State Sequences in query by example

Reduction of Speech Data Posteriorgrams by Compressing Maxim...

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Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Yokota, Takashi Kojima, Kazunori Lee, Shi-wook Itoh, Yoshiaki Iwate Prefectural Univ Takizawa Iwate Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9789881476883

Spoken-term detection (STD) has recently attracted increased interest in speech-based retrieval research. STD is the task of finding sections in speech data matching a query consisting of one or more words. query by example (QbE) using spoken queries is another important research topic in STD. Although the use of posteriorgrams (sequences of output probabilities generated by deep neural networks from speech data) is a promising approach for QbE, that method results in long retrieval times and excessive memory usage. We previously proposed a method that replaces posteriorgrams for spoken queries with sequences of state numbers for triphone hidden Markov models, omitting calculations of local distance. While that method greatly reduced retrieval times, it still required large amounts of memory to store speech data posteriorgrams. We therefore newly propose a method for reducing memory usage and retrieval times by compressing speech data posteriorgrams into sets of posterior probability vectors for each utterance, each speech document, or all speech data, rather than storing all posterior probability vectors for each frame of speech data. Evaluation experiments conducted using open test collections for the "SpokenDoc" tasks of the NTCIR-10 and NTCIR-12 workshops demonstrate memory usage reduction by the proposed method.

关键词： Spoken-term detection query by example Average posterior probability vector

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APPLYING query by example IN OCL FOR PLATFORM-INDEPENDENT PROGRAMMING

APPLYING QUERY BY EXAMPLE IN OCL FOR PLATFORM-INDEPENDENT PR...

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6th International Conference on Web Information Systems and Technologies

作者： Falda, Grzegorz Filipowicz, Wiktor Habela, Piotr Stencel, Krzysztof Subieta, Kazimierz Kaczmarski, Krzysztof Polish Japanese Inst Informat Technol Warsaw Poland Warsaw Univ Technol Warsaw Poland

ISBN: (纸本)9789896740252

Precise modelling of behaviour is an area where programming meets modelling, and textual syntax competes with a visual one. By developing a UML based platform-independent framework, we aimed to find a visual syntax aid to make the language more approachable to stakeholders, while taking advantage of existing UML syntax intuitions and offering a truly higher level of abstraction. Our solution consists of seamlessly integrated UML Actions and the Object Constraint Language (OCL) as a database query language, featuring both a textual and a visual syntax. In this paper we describe a declarative, query by example (QBE)-based approach to visualizing OCL expressions over a UML object-oriented model instance, to be used inside of textual or visual imperative statements. Such visual OCL expressions can also be used as ad-hoc queries. The paper presents a choice of visual syntax and describes its underlying semantics.

关键词： Database Object-oriented query language Visual query by example Model driven architecture UML OCL

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Fuzzy query by example 18

Fuzzy Query By Example

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33rd Annual ACM Symposium on Applied Computing (ACM SAC)

作者： Moreau, Aurelien Pivert, Olivier Smits, Gregory Univ Rennes CNRS IRISA UMR 6074 F-22305 Lannion France

ISBN: (纸本)9781450351911

This paper describes Fuzzy query by example, an approach helping users retrieve data without any prior knowledge of the database schema or any formal querying language. The user is solicited to evaluate, in a binary way, pre-selected items of the database. We provide a characterization-based strategy that identifies the properties shared by the examples (resp. counter-examples) positively (resp. negatively) evaluated by the user. These properties are expressed using linguistic terms from a fuzzy vocabulary to ensure that the user has a good understanding of the inferred query.

关键词： query by example Fuzzy logic Databases

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