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11th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Wu, Haiwei Li, Ming Cai, Zexin Zhong, Haibin Duke Kunshan Univ Kunshan Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Guangdong Peoples R China Jiangsu Jinling Sci & Technol Grp Ltd Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538656273

In the task of the unsupervised query by example spoken term detection (QbE-STD), we concatenate the features extracted by a Self-Organizing Map (SOM) and features learned by an unsupervised GMM based model at the feature level to enhance the performance. More specifically, The SOM features are represented by the distances between the current feature vector and the weight vectors of SOM neurons learned in an unsupervised manner. After fetching these features, we apply sub-sequence Dynamic Time Warping (S-DTW) to detect the occurrences of keywords in the test data. We evaluate the performance of these features on the TIMIT English database. After concatenating the SOM features and the GMM based features together, we achieve an improvement of 7.77% and 7.74% on Mean Average Precision (MAP) and P@10 on average.

关键词： unsupervised learning query by example self-organizing map

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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

作者： D. Espinoza-Molina M. Quartulli M. Datcu German Aerospace Center Remote Sensing Technology Institute Visual Interaction and Communication Technologies Centre

ISBN: (纸本)9781467311601

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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Webpage retrieval based on query by example for think tank construction

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INFORMATION PROCESSING & MANAGEMENT 2022年第1期59卷 102767-102767页

作者： Geng, Qian Chuai, Ziang Jin, Jian Beijing Normal Univ Zhuhai Ctr Governance Studies Zhuhai Peoples R China Beijing Normal Univ Sch Govt Dept Informat Management Beijing Peoples R China

Think tanks have been proved helpful for decision-making in various communities. However, collecting information manually for think tank construction implies too much time and labor cost as well as inevitable subjectivity. A probable solution is to retrieve webpages of renowned experts and institutes similar to a given example, denoted as query by webpage (QBW). Considering users' searching behaviors, a novel QBW model based on webpages' visual and textual features is proposed. Specifically, a visual feature extraction module based on pre trained neural networks and a heuristic pooling scheme is proposed, which bridges the gap that existing extractors fail to extract snapshots' high-level features and are sensitive to the noise effect brought by images. Moreover, a textual feature extraction module is proposed to represent textual content in both term and topic grains, while most existing extractors merely focus on the term grain. In addition, a series of similarity metrics are proposed, including a textual similarity metric based on feature bootstrapping to improve model's robustness and an adaptive weighting scheme to balance the effect of different types of features. The proposed QBW model is evaluated on expert and institute introduction retrieval tasks in academic and medical scenarios, in which the average value of MAP has been improved by 10% compared to existing baselines. Practically, useful insights can be derived from this study for various applications involved with webpage retrieval besides think tank construction.

关键词： Feature bootstrapping Pre-trained neural networks query by example Textual features Visual features Webpage retrieval

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Bridging the gap: query by semantic example

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IEEE TRANSACTIONS ON MULTIMEDIA 2007年第5期9卷 923-938页

作者： Rasiwasia, Nikhil Moreno, Pedro J. Vasconcelos, Nuno Univ Calif San Diego Dept Elect & Comp Engn Stat Visual Comp Lab La Jolla CA 92093 USA Google Inc Google Res & Dev Ctr New York NY 10011 USA

A combination of query-by-visual-example (QBVE) and semantic retrieval (SR), denoted as query-by-semantic-example (QBSE), is proposed. Images are labeled with respect to a vocabulary of visual concepts, as is usual in SR. Each image is then represented by a vector, referred to as a semantic multinomial, of posterior concept probabilities. Retrieval is based on the query-by-example paradigm: the user provides a query image, for which 1) a semantic multinomial is computed and 2) matched to those in the database. QBSE is shown to have two main properties of interest, one mostly practical and the other philosophical. From a practical standpoint, because it inherits the generalization ability of SR inside the space of known visual concepts (referred to as the semantic space) but performs much better outside of it, QBSE produces retrieval systems that are more accurate than what was previously possible. Philosophically, because it allows a direct comparison of visual and semantic representations under a common query paradigm, QBSE enables the design of experiments that explicitly test the value of semantic representations for image retrieval. An implementation of QBSE under the minimum probability of error (MPE) retrieval framework, previously applied with success to both QBVE and SR, is proposed, and used to demonstrate the two properties. In particular, an extensive objective comparison of QBSE with QBVE is presented, showing that the former significantly outperforms the latter both inside and outside the semantic space. By carefully controlling the structure of the semantic space, it is also shown that this improvement can only be attributed to the semantic nature of the representation on which QBSE is based.

关键词： content-based image retrieval Gaussian mixtures image similarity multiple instance learning query by example semantic retrieval semantic space

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Real-time selection of video streams for live TV broadcasting based on query-by-example using a 3D model

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第8期74卷 2659-2685页

作者： Yus, Roberto Ilarri, Sergio Mena, Eduardo Univ Zaragoza IIS Dept Zaragoza 50018 Spain

The emergence of low-cost cameras with nearly professional features in the consumer market represents a new important source of video information. For example, using an increasing number of these cameras in live TV broadcastings enables obtaining varied contents without affecting the production costs. However, searching for interesting shots (e.g., a certain view of a specific car in a race) among many video sources in real-time can be difficult for a Technical Director (TD). So, TDs require a mechanism to easily and precisely represent the kind of shot they want to obtain abstracting them from the need to be aware of all the views provided by the cameras. In this paper we present our proposal to help a TD to visually define, using an interface for the definition of 3D scenes, an interesting sample view of one or more objects in the scenario. We recreate the views of the cameras in a 3D engine and apply 3D geometric computations on their virtual view, instead of analyzing the real images they provide, to enable an efficient and precise real-time selection. Specifically, our system computes a similarity measure to rank the candidate cameras. Moreover, we present a prototype of the system and an experimental evaluation that shows the interest of our proposal.

关键词： query by example Camera shot similarity computation User interfaces to manage 3D scenes

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query-by-example Spoken Term Detection Using Low Dimensional Posteriorgrams Motivated by Articulatory Classes 17

Query-by-Example Spoken Term Detection Using Low Dimensional...

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IEEE 17th International Workshop on Multimedia Signal Processing (MMSP 2015)

作者： Popli, Abhimanyu Kumar, Arun Indian Inst Technol Delhi Ctr Appl Res Elect New Delhi 110016 India Ctr Dev Telemat New Delhi 110030 India

ISBN: (纸本)9781467374781

This paper addresses the problem of query-by-example Spoken Term Detection (QbE-STD). Posteriorgrams have been widely used in the research on QbE-STD. Features based on articulatory classes are known to be robust to phonemic variations. The articulatory features like voicing and place of articulation are the main distinguishing features among some plosives and fricatives. These properties inspire the study of posteriorgrams based on articulatory classes for QbE-STD. Most of the previous works based on articulatory features have defined a large number of articulatory classes making it difficult to use them directly for pattern matching. Also, most of the works have completely ignored the uniqueness of the phonemes having transitory places of articulation eg. diphthongs and approximants. These issues have been addressed in this work while carefully selecting low dimensional articulatory motivated (LDAM) posteriorgrams on the basis of detailed experiments. This work is the first to show that the articulatory based posteriorgrams can outperform the phonemic posteriorgram significantly in a stand alone way (without any support from acoustic or phonemic features) for the task of QbE-STD.

关键词： query by example Joints Voice Phonemes DIMENSIONAL Acoustics uniqueness

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Multiple Deep Learning Models and Architectures with Different Numbers of States Used to Improve Retrieval Accuracy of query-by-example

Multiple Deep Learning Models and Architectures with Differe...

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

作者： Hatakeyama, Kazuki Nishino, Masahiro Kojima, Kazunori Lee, Shi-wook Itoh, Yoshiaki Iwate Prefectural Univ Takizawa Japan Toyota Syst Corp Tokyo Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9789881476890

Studies examining Spoken Term Detection (STD) and Spoken query STD (SQ-STD) or query by example (QbE) using a spoken query have been conducted actively in recent years. When a spoken query is transcribed into a text using an automatic speech recognizer in SQ-STD, some misrecognition leads to retrieval accuracy deterioration. Posteriorgrams obtained using Deep Neural Network (DNN) and so on can be regarded as speaker-independent features. Although posteriorgram matching between a posteriorgram of a spoken query and posteriorgram of speech data showed high retrieval accuracy, it requires a long retrieval time and a large memory space. In earlier papers, we proposed a maximum likelihood state sequence method (MLSS) for retrieval time reduction. As described herein, we propose a method for reducing both the retrieval time and the memory space using MLSS method and multiple machine learning models with different numbers of states. The models show heterogeneous retrieval results. Their integration is probably mutually complementary and engenders retrieval accuracy improvement. Evaluation results demonstrate that the proposed method improves the retrieval accuracy, thereby reducing the retrieval time and the memory space.

关键词： query by example Spoken term detection maximum likelihood state sequence

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COMBINING TEMPORAL AND SPECTRAL INFORMATION FOR query-BY-example SPOKEN TERM DETECTION 22

COMBINING TEMPORAL AND SPECTRAL INFORMATION FOR QUERY-BY-EXA...

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22nd European Signal Processing Conference (EUSIPCO)

作者： Gracia, Ciro Anguera, Xavier Binefa, Xavier Univ Pompeu Fabra Dept Informat & Commun Technol Barcelona Spain Telefon Res Barcelona 08019 Spain

ISBN: (纸本)9780992862619

We present a system for query-by-example Spoken Term Detection on zero-resource languages. The system compares speech patterns by representing the signal using two different acoustic models, a Spectral Acoustic (SA) model covering the spectral characteristics of the signal, and a Temporal Acoustic (TA) model covering the temporal evolution of the speech signal. Given a query and a utterance to be compared, first we compute their posterior probabilities according to each of the two models, compute similarity matrices for each model and combine these into a single enhanced matrix. Subsequence-Dynamic Time Warping (S-DTW) algorithm is used to find optimal subsequence alignment paths on this final matrix. Our experiments on data from the 2013 Spoken Web Search (SWS) task at Mediaeval benchmark evaluation show that this approach provides state of the art results and significantly improves both the single model strategies and the standard metric baselines.

关键词： query by example zero resources languages unsupervised learning long temporal context

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Capturing the human action semantics using a query-by-example

Capturing the human action semantics using a query-by-exampl...

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International Conference on Signal Processing and Multimedia Applications

作者： Montesanto, Anna Baldassarri, Paola Dragoni, A. F. Vallesi, G. Puliti, P. Univ Politecn Marche DEIT Ancona Italy

ISBN: (纸本)9789898111609

The paper describes a method for extracting human action semantics in video's using queries-by-example. Here we consider the indexing and the matching problems of content-based human motion data retrieval. The query formulation is based on trajectories that may be easily built or extracted by following relevant points on a video, by a novice user too. The so realized trajectories contain high value of action semantics. The semantic schema is built by splitting a trajectory in time ordered sub-sequences that contain the features of extracted points. This kind of semantic representation allows reducing the search space dimensionality and, being human-oriented, allows a selective recognition of actions that are very similar among them. A neural network system analyzes the video semantic similarity, using a two-layer architecture of multilayer perceptrons, which is able to learn the semantic schema of the actions and to recognize them.

关键词： query by example human action semantics artificial intelligent systems neural networks

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Acoustic Word Embedding System for Code-Switching query-by-example Spoken Term Detection 12

Acoustic Word Embedding System for Code-Switching Query-by-e...

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12th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Ma, Murong Wu, Haiwei Wang, Xuyang Yang, Lin Wang, Junjie Li, Ming Duke Kunshan Univ Data Sci Res Ctr Kunshan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China AI Lab Lenovo Res Beijing Peoples R China

ISBN: (纸本)9781728169941

In this paper, we propose a deep convolutional neural network-based acoustic word embedding system for code-switching query by example spoken term detection. Different from previous configurations, we combine audio data in two languages for training instead of only using one single language. We transform the acoustic features of keyword templates and searching content segments obtained in a sliding manner to fixed-dimensional vectors and calculate the distances between them. An auxiliary variability-invariant loss is also applied to training data within the same word but different speakers. This strategy is used to prevent the extractor from encoding undesired speaker- or accent-related information into the acoustic word embeddings. Experimental results show that our proposed system produces promising searching results in the code-switching test scenario. With the employment of variability-invariant loss, the searching performance is further enhanced.

关键词： convolutional neural network acoustic word embedding code-switching query by example

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