Toward an Automatic Evaluation of Retrieval Performance with Large Scale Image Collections

2015 ◽  
Author(s):  
Adrian Popescu ◽  
Eleftherios Spyromitros-Xioufis ◽  
Symeon Papadopoulos ◽  
Hervé Le Borgne ◽  
Ioannis Kompatsiaris
Keyword(s):  
Large Scale ◽  
Automatic Evaluation ◽  
Retrieval Performance

An Emotional Scene Retrieval Framework for Lifelog Videos Using Ensemble Clustering

2015 ◽  
Vol 3 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Hiroki Nomiya ◽  
Atsushi Morikuni ◽  
Teruhisa Hochin
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Video Data ◽  
Training Data ◽  
Expression Recognition ◽  
Learning Approaches ◽  
Ensemble Clustering ◽  
Retrieval Performance ◽  
Scene Detection ◽  
Emotional Scenes

A lifelog video retrieval framework is proposed for the better utilization of a large amount of lifelog video data. The proposed method retrieves emotional scenes such as the scenes in which a person in the video is smiling, considering that a certain important event could happen in most of emotional scenes. The emotional scene is detected on the basis of facial expression recognition using a wide variety of facial features. The authors adopt an unsupervised learning approach called ensemble clustering in order to recognize the facial expressions because supervised learning approaches require sufficient training data, which make it quite troublesome to apply to large-scale video databases. The retrieval performance of the proposed method is evaluated by means of an emotional scene detection experiment from the viewpoints of accuracy and efficiency. In addition, a prototype retrieval system is implemented based on the proposed emotional scene detection method.

Common Data Format Archiving of Large-Scale Intelligent Transportation Systems Data for Efficient Storage, Retrieval, and Portability

2003 ◽  
Vol 1836 (1) ◽  
pp. 111-117
Author(s):  
Taek M. Kwon ◽  
Nirish Dhruv ◽  
Siddharth A. Patwardhan ◽  
Eil Kwon
Keyword(s):  
Sensor Networks ◽  
Large Scale ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Sensor Data ◽  
Traffic Data ◽  
Retrieval Performance ◽  
Science Data ◽  
Data Format ◽  
Data Archiving

Intelligent transportation system (ITS) sensor networks, such as road weather information and traffic sensor networks, typically generate enormous amounts of data. As a result, archiving, retrieval, and exchange of ITS sensor data for planning and performance analysis are becoming increasingly difficult. An efficient ITS archiving system that is compact and exchangeable and allows efficient and fast retrieval of large amounts of data is essential. A proposal is made for a system that can meet the present and future archiving needs of large-scale ITS data. This system is referred to as common data format (CDF) and was developed by the National Space Science Data Center for archiving, exchange, and management of large-scale scientific array data. CDF is an open system that is free and portable and includes self-describing data abstraction. Archiving traffic data by using CDF is demonstrated, and its archival and retrieval performance is presented for the Minnesota Department of Transportation–s 30-s traffic data collected from about 4,000 loop detectors around Twin Cities freeways. For comparison of the archiving performance, the same data were archived by using a commercially available relational database, which was evaluated for its archival and retrieval performance. This result is presented, along with reasons that CDF is a good fit for large-scale ITS data archiving, retrieval, and exchange of data.

scholarly journals Requirements for Large Scale Web Accessibility Evaluation

2020 ◽  
pp. 275-283
Author(s):  
Fabio Paternò ◽  
Francesca Pulina ◽  
Carmen Santoro ◽  
Henrike Gappa ◽  
Yehya Mohamad
Keyword(s):  
Large Scale ◽  
Web Applications ◽  
Web Accessibility ◽  
Online Information ◽  
Automatic Evaluation ◽  
Web Resources ◽  
Accessibility Evaluation ◽  
Accessibility Issues ◽  
Evaluation And Monitoring ◽  
Over Time

Abstract The recent European legislation emphasizes the importance of enabling people with disabilities to have access to online information and services of public sector bodies. To this regard, automatic evaluation and monitoring of Web accessibility can play a key role for various stakeholders involved in creating and maintaining over time accessible products. In this paper we present the results of elicitation activities that we carried out in a European project to collect experience and feedback from Web commissioners, developers and content authors of websites and web applications. The purpose was to understand their current practices in addressing accessibility issues, identify the barriers they encounter when exploiting automatic support in ensuring the accessibility of Web resources, and receive indications about what functionalities they would like to exploit in order to better manage accessibility evaluation and monitoring.

Interpreting Results from Large Scale Automatic Evaluation of Web Accessibility

2006 ◽  
pp. 184-191 ◽  
Author(s):  
Christian Bühler ◽  
Helmut Heck ◽  
Olaf Perlick ◽  
Annika Nietzio ◽  
Nils Ulltveit-Moe
Keyword(s):  
Large Scale ◽  
Web Accessibility ◽  
Automatic Evaluation

scholarly journals Predicting the Quality of Short Narratives from Social Media

2017 ◽  
Author(s):  
Tong Wang ◽  
Ping Chen ◽  
Boyang Li
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Large Scale ◽  
Computational Models ◽  
Quality Evaluation ◽  
Future Research ◽  
Automatic Evaluation ◽  
Need To Evaluate ◽  
Difficult Challenge

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.

scholarly journals Similarity Preserving Deep Asymmetric Quantization for Image Retrieval

2019 ◽  
Vol 33 ◽  
pp. 8183-8190 ◽  
Author(s):  
Junjie Chen ◽  
William K. Cheung
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Multimedia Retrieval ◽  
Retrieval Performance ◽  
Retrieval Accuracy ◽  
Compact Binary ◽  
Label Information ◽  
Benchmark Datasets ◽  
Similarity Preserving ◽  
Novel Model

Quantization has been widely adopted for large-scale multimedia retrieval due to its effectiveness of coding highdimensional data. Deep quantization models have been demonstrated to achieve the state-of-the-art retrieval accuracy. However, training the deep models given a large-scale database is highly time-consuming as a large amount of parameters are involved. Existing deep quantization methods often sample only a subset from the database for training, which may end up with unsatisfactory retrieval performance as a large portion of label information is discarded. To alleviate this problem, we propose a novel model called Similarity Preserving Deep Asymmetric Quantization (SPDAQ) which can directly learn the compact binary codes and quantization codebooks for all the items in the database efficiently. To do that, SPDAQ makes use of an image subset as well as the label information of all the database items so the image subset items and the database items are mapped to two different but correlated distributions, where the label similarity can be well preserved. An efficient optimization algorithm is proposed for the learning. Extensive experiments conducted on four widely-used benchmark datasets demonstrate the superiority of our proposed SPDAQ model.

scholarly journals Adversarial Attack on Deep Product Quantization Network for Image Retrieval

2020 ◽  
Vol 34 (07) ◽  
pp. 10786-10793 ◽  
Author(s):  
Yan Feng ◽  
Bin Chen ◽  
Tao Dai ◽  
Shu-Tao Xia
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Visual Features ◽  
Retrieval Performance ◽  
Retrieval Models ◽  
Product Quantization ◽  
Security Issues ◽  
Retrieval Systems ◽  
Adversarial Examples ◽  
Adversarial Attack

Deep product quantization network (DPQN) has recently received much attention in fast image retrieval tasks due to its efficiency of encoding high-dimensional visual features especially when dealing with large-scale datasets. Recent studies show that deep neural networks (DNNs) are vulnerable to input with small and maliciously designed perturbations (a.k.a., adversarial examples). This phenomenon raises the concern of security issues for DPQN in the testing/deploying stage as well. However, little effort has been devoted to investigating how adversarial examples affect DPQN. To this end, we propose product quantization adversarial generation (PQ-AG), a simple yet effective method to generate adversarial examples for product quantization based retrieval systems. PQ-AG aims to generate imperceptible adversarial perturbations for query images to form adversarial queries, whose nearest neighbors from a targeted product quantizaiton model are not semantically related to those from the original queries. Extensive experiments show that our PQ-AQ successfully creates adversarial examples to mislead targeted product quantization retrieval models. Besides, we found that our PQ-AG significantly degrades retrieval performance in both white-box and black-box settings.

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