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Subgraph spotting in graph representations of comic book images

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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SQuID: Semantic Similarity-Aware query Intent Discovery 18

SQuID: Semantic Similarity-Aware Query Intent Discovery

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44th ACM SIGMOD International Conference on Management of Data

作者： Fariha, Anna Sarwar, Sheikh Muhammad Meliou, Alexandra Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9781450317436

Recent expansion of database technology demands a convenient framework for non-expert users to explore datasets. Several approaches exist to assist these non-expert users where they can express their query intent by providing example tuples for their intended query output. However, these approaches treat the structural similarity among the example tuples as the only factor specifying query intent and ignore the richer context present in the data. In this demo, we present SQUID, a system for Semantic similarity aware query Intent Discovery. SQUID takes a few example tuples from the user as input, through a simple interface, and consults the database to discover deeper associations among these examples. These data-driven associations reveal the semantic context of the provided examples, allowing SQUID to infer the user's intended query precisely and effectively. SQUID further explains its inference, by displaying the discovered semantic context to the user, who can then provide feedback and tune the result. We demonstrate how SQUID can capture even esoteric and complex semantic contexts, alleviating the need for constructing complex SQL queries, while not requiring the user to have any schema or query language knowledge.

关键词： SQL query discovery semantic similarity query by example

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Combining Acoustic and Semantic Similarity for Acoustic Scene Retrieval

Combining Acoustic and Semantic Similarity for Acoustic Scen...

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IEEE International Symposium on Multimedia

作者： Mustafa Sert Ahmet Melih Başbuğ Department of Computer Engineering Başkent University Ankara TURKEY Baskent University Ankara Ankara Turkey

ISBN: (数字)9781728156064

ISBN: (纸本)9781728156071

Automatic retrieval of acoustic scenes in large audio collections is a challenging task due to the complex structures of these sounds. A robust and flexible retrieval system should address both the acoustic-and semantic aspects of these sounds and how to combine them. In this study, we introduce an acoustic scene retrieval system that uses a combined acoustic-and semantic-similarity method. To address the acoustic aspects of sound scenes, we use a cascaded convolutional neural network (CNN) with a gated recurrent unit (GRU). The acoustic similarity is calculated in feature space using the Euclidean distance and the semantic similarity is obtained using the Path Similarity method of the WordNet. Two performance datasets from the TAU Urban Acoustic Scenes 2019 and the TUT Urban Acoustic Scenes 2018 are used to compare the performance of the proposed retrieval system with the literature and the developed baseline. Results show that the semantic similarity improves the mAP and P@k scores.

关键词： Acoustic scene retrieval Semantic similarity Acoustic similarity Convolutional neural network (CNN) Gated recurrent unit (GRU) query-By-example Acoustics Search engines query by example Semantics SOUND Euclidean distance similarity method Eigenvector retrieval complex structure

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OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations

OCGAN: One-class Novelty Detection Using GANs with Constrain...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Pramuditha Perera Ramesh Nallapati Bing Xiang Johns Hopkins University AWS AI

ISBN: (纸本)9781728132945

We present a novel model called OCGAN for the classical problem of one-class novelty detection, where, given a set of examples from a particular class, the goal is to determine if a query example is from the same class. Our solution is based on learning latent representations of in-class examples using a denoising auto-encoder network. The key contribution of our work is our proposal to explicitly constrain the latent space to exclusively represent the given class. In order to accomplish this goal, firstly, we force the latent space to have bounded support by introducing a tanh activation in the encoder's output layer. Secondly, using a discriminator in the latent space that is trained adversari-ally, we ensure that encoded representations of in-class examples resemble uniform random samples drawn from the same bounded space. Thirdly, using a second adversarial discriminator in the input space, we ensure all randomly drawn latent samples generate examples that look real. Finally, we introduce a gradient-descent based sampling technique that explores points in the latent space that generate potential out-of-class examples, which are fed back to the network to further train it to generate in-class examples from those points. The effectiveness of the proposed method is measured across four publicly available datasets using two one-class novelty detection protocols where we achieve state-of-the-art results.

关键词： Novelty representations gallium nitrate query by example ENCODER

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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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GRAPH INDEX BASED query-BY-example SEARCH ON A LARGE SPEECH DATA SET

GRAPH INDEX BASED QUERY-BY-EXAMPLE SEARCH ON A LARGE SPEECH ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Kazuo Aoyama Atsunori Ogawa Takashi Hattori Takaaki Hori Atsushi Nakamura NTT Communication Science Laboratories NTT Corporation 2-4 Hikaridai Seika-cho Soraku-gun Kyoto 619-0237 Japan

ISBN: (纸本)9781479903573

This paper presents a neighborhood graph index approach for query-by-example search using dynamic time warping (DTW) on Gaussian mixture model (GMM) posteriorgram sequences. The approach is intended to achieve a significant speed-up of a spoken term detection (STD) task for resource-limited situations. The proposed method employs a degree-reduced k-nearest neighbor (k-DR) graph as an index. A set of k-DR graphs is pre-constructed off-line from a large number of GMM posteriorgram sequences. Given a query posteriorgram sequence, one k-DR graph is selected from the set as the index. By applying a newly introduced combination of greedysearch (GS) and breadth-first search (BFS) algorithms to the selected k-DR graph index, the proposed method efficiently achieves query-by-example STD. Experimental results on the MIT lecture corpus demonstrate that the proposed method works much faster than a state-of-art method by more than one order magnitude, keeping almost the same precision.

关键词： Neighborhood graph index Spoken term detection query-by-example search Dynamic time warping Gaussian mixture model posteriorgram dynamic time warping query by example Line graph Speech Gm(m) LARGE gene Sexually Transmitted Diseases SET

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CONTENT-BASED REPRESENTATIONS OF AUDIO USING SIAMESE NEURAL NETWORKS

CONTENT-BASED REPRESENTATIONS OF AUDIO USING SIAMESE NEURAL ...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Manocha, Pranay Badlani, Rohan Kumar, Anurag Shah, Ankit Elizalde, Benjamin Raj, Bhiksha IIT Guwahati Dept Elect & Elect Engn Gauhati India BITS Pilani Dept Comp Sci Hyderabad Telangana India Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781538646588

In this paper, we focus on the problem of content-based retrieval for audio, which aims to retrieve all semantically similar audio recordings for a given audio clip query. This problem is similar to the problem of query by example of audio, which aims to retrieve media samples from a database, which are similar to the user-provided example. We propose a novel approach which encodes the audio into a vector representation using Siamese Neural Networks. The goal is to obtain an encoding similar for files belonging to the same audio class, thus allowing retrieval of semantically similar audio. Using simple similarity measures such as those based on simple euclidean distance and cosine similarity we show that these representations can be very effectively used for retrieving recordings similar in audio content.

关键词： Audio Fingerprinting Content-Based Retrieval query by example Siamese Network Similar Matching

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An Intelligent System for Spoken Term Detection That Uses Belief Combination

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IEEE INTELLIGENT SYSTEMS 2017年第1期32卷 70-79页

作者： Khan, Wasiq Kuru, Kaya Univ Cent Lancashire Sch Engn Preston PR1 2HE Lancs England

Spoken term detection (STD) can be considered a subpart of automatic speech recognition that aims to extract partial information from speech signals in the form of query utterances. A variety of STD techniques available in the literature employ a single source of evidence for query utterance match/mismatch determination. In this article, the authors develop an acoustic signal processing-based approach for STD that incorporates several techniques for silence removal, dynamic noise filtration, and evidence combination using the Dempster-Shafer theory. Spectral-temporal features-based voiced segment detection and energy and zero cross rate-based unvoiced segment detection remove silence segments in the speech signal. Comprehensive experiments have been performed on large speech datasets and satisfactory results have been achieved with the proposed approach, which improves existing speaker-dependent STD approaches, specifically the reliability of query utterance spotting, by combining the results from multiple belief sources.

关键词： Automatic Speech Recognition Mel Frequency Cepstral Coefficient Speech Recognition Feature Extraction Mathematical Model Euclidean Distance Time Frequency Analysis Intelligent Systems Spoken Term Detection Acoustic Keyword Spotting query by example Dempster Shafer Theory Speech Recognition Speech Processing

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Content-Based Representations of Audio Using Siamese Neural Networks

Content-Based Representations of Audio Using Siamese Neural ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Pranay Manocha Rohan Badlani Anurag Kumar Ankit Shah Benjamin Elizalde Bhiksha Raj Department of Electronics and Electrical Engineering IIT Guwahati India Department of Computer Science BITS Pilani India Language Technologies Institute Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9781538646595

关键词： Audio Fingerprinting Content-Based Retrieval query by example Siamese Network Similar Matching Audio Content retrieval query by example vector representation siamese Audio recordings Euclidean distance Similar representations

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Synthesizing Highly Expressive SQL Queries from Input-Output examples 2017

Synthesizing Highly Expressive SQL Queries from Input-Output...

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38th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)

作者： Wang, Chenglong Cheung, Alvin Bodik, Rastislav Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450349888

SQL is the de facto language for manipulating relational data. Though powerful, SQL queries can be difficult to write due to their highly expressive constructs. Using the programming-by-example paradigm to help users write SQL queries presents an attractive proposition, as evidenced by online help forums such as Stack Overflow. However, developing techniques to synthesize SQL queries from input-output (I/O) examples has been difficult due to SQL's rich set of operators. In this paper, we present a new scalable and efficient algorithm to synthesize SQL queries from I/O examples. Our key innovation is the development of a language for abstract queries, i.e., queries with uninstantiated operators, that can express a large space of SQL queries efficiently. Using abstract queries to represent the search space nicely decomposes the synthesis problem into two tasks: (1) searching for abstract queries that can potentially satisfy the given I/O examples, and (2) instantiating the found abstract queries and ranking the results. We implemented the algorithm in a new tool, called SCYTHE, and evaluated it on 193 benchmarks collected from Stack Overflow. Our results showed that SCYTHE efficiently solved 74% of the benchmarks, most in just a few seconds. Queries synthesized by SCYTHE range from simple ones involving a single selection to complex ones with six levels of nested queries.

关键词： Program Synthesis query by example SQL

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