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IntentSearch: Capturing User Intention for One-Click Internet Image Search

Xiaoou Tang, Ke Liu, Jingyu Cui, Fang Wen, Xiaogang Wang
2012 IEEE Transactions on Pattern Analysis and Machine Intelligence  
It only requires the user to click on one query image with minimum effort and images from a pool retrieved by text-based search are reranked based on both visual and textual content.  ...  4) Expanded keywords are also used to expand the query image to multiple positive visual examples from which new query specific visual and textual similarity metrics are learned to further improve content-based  ...  For a web-scale commercial system, users' feedback has to be limited to the minimum, such as one-click feedback.  ... 
doi:10.1109/tpami.2011.242 pmid:22156100 fatcat:jdy234e23raoddgo2ftz3ezxbu

Web-Based Student Opinion Mining System Using Sentiment Analysis

Olaniyi Abiodun Ayeni, Department of Cyber Security, Federal University of Technology, Akure, Nigeria, Akinkuotu Mercy, Thompson A.F, Mogaji A.S
2020 International Journal of Information Engineering and Electronic Business  
of analysing the feedback collected from students.  ...  The major tools used in developing this application are Python, Scikit learn, Textblob, Pandas and SQLite.. Django provides an in-built server that allows the application to run on the localhost..  ...  removed, then topics are extracted for clustering Limitation: Semantic similarity for clustering the student feedback was not considered in the paper.  ... 
doi:10.5815/ijieeb.2020.05.04 fatcat:zrmy5io4oranvpou2hazc4kp6e

Image retrieval

Ritendra Datta, Dhiraj Joshi, Jia Li, James Z. Wang
2008 ACM Computing Surveys  
However, mobile users can be expected to provide only limited feedback. Hence it becomes necessary to design intelligent feedback methods to cater to users with small displays.  ...  Feature Extraction Global extraction Prior knowledge Training Learning User feedback Local extraction ......  ... 
doi:10.1145/1348246.1348248 fatcat:5jbcrsxkkbac5cya3zb7eb22ea

Helping Users Sort Faster with Adaptive Machine Learning Recommendations [chapter]

Steven M. Drucker, Danyel Fisher, Sumit Basu
2011 Lecture Notes in Computer Science  
Sorting and clustering large numbers of documents can be an overwhelming task: manual solutions tend to be slow, while machine learning systems often present results that don't align well with users' intents  ...  The recommendations are based on a learning model that adapts over time -as the user adds more items to a cluster, the system's model improves and the recommendations become more relevant.  ...  Methods of combining machine learning with manual clustering have met with mixed results.  ... 
doi:10.1007/978-3-642-23765-2_13 fatcat:6ei6cl6zlzdfretf3fvzod6x5y

PWIS: Personalized Web Image Search using One-Click Method

Pushpa C.N., Vinay Kumar, Thriveni J., Venugopal K.R.
2015 International Journal of Computer Applications  
It only needs the user to click on one query image with minimal effort and images from a pool fetched by text-based search are re-ranked based on both visual and textual contents.  ...  To retrieve exact matching, and acquire user"s intention we can allow them text query with extended or related images as a suggestion. We have proposed an innovative Web image search approach.  ...  Relevance feedback required more users" effort. For a webscale commercial system, users" feedback has to be limited to the minimum, such as one-click feedback.  ... 
doi:10.5120/ijca2015907101 fatcat:lwd2kehjl5dovb6jzkv7lchdwy

Enhancing semi-supervised document clustering with feature supervision

Yeming Hu, Evangelos E. Milios, James Blustein
2012 Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12  
Traditional semi-supervised clustering uses only limited user supervision in the form of labeled instances and pairwise instance constraints to aid unsupervised clustering.  ...  This paper thus fills this void by enhancing traditional semi-supervised clustering with feature supervision which asks the user to label discriminating features during labeling the instance or pairwise  ...  [13] make use of feature feedback in the active learning with support vector machine by upweighting the accepted features.  ... 
doi:10.1145/2245276.2245457 dblp:conf/sac/HuMB12a fatcat:kdczeyjp3ba4famcg4hyvkw3ce

SoK: Applying Machine Learning in Security - A Survey [article]

Heju Jiang, Jasvir Nagra, Parvez Ahammad
2016 arXiv   pre-print
The idea of applying machine learning(ML) to solve problems in security domains is almost 3 decades old.  ...  , clustering Local sequence alignment, IDF, hierarchical clustering(metric: Jaccard similarity) Smutz NDSS'16 [113] Detect malware mimicry evasions with ensemble classifiers Two malware classifiers as  ...  Behavioral metric for user verification by mouse movement SVM(RBF kernel) Brusztein CCS'11 [68] Text-based CAPTCHA strength and weakness.  ... 
arXiv:1611.03186v1 fatcat:hfvc5hhu7ze77lrnjufslcg6gm

Telescope: an interactive tool for managing large scale analysis from mobile devices [article]

Jaqueline J. Brito, Thiago Mosqueiro, Jeremy Rotman, Victor Xue, Douglas J. Chapski, Juan De la Hoz, Paulo Matias, Lana Martin, Alex Zelikovsky, Matteo Pellegrinni, Serghei Mangul
2019 arXiv   pre-print
Here we report the development of Telescope, a novel interactive tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring  ...  Telescope was designed to natively operate with a simple and straightforward interface using Web 2.0 technology compatible with most modern devices (e.g., tablets and personal smartphones).  ...  The User Interface relies primarily on both the Local Database and the Rate Limiter to render all relevant job information into a mobile-friendly web page while limiting the impact of Telescope's interaction  ... 
arXiv:1909.12469v3 fatcat:2iqbwvh66bhavct55wilpqwxla

Where2Change: Change Request Localization for App Reviews

Tao Zhang, Jiachi Chen, Xian Zhan, Xiapu Luo, David Lo, He Jiang
2019 IEEE Transactions on Software Engineering  
., user feedback) from user reviews and identifies user feedback related to various problems and feature requests, and then cluster the corresponding user feedback into groups.  ...  similarity metric.  ...  In this work, we utilize issue reports to enrich user feedback clusters for improving the performance of change request localization. This is the difference with CHANGEADVISOR.  ... 
doi:10.1109/tse.2019.2956941 fatcat:ncixeedtezccrilclgk7z2iy3u

A survey of browsing models for content based image retrieval

Daniel Heesch
2008 Multimedia tools and applications  
Often, relevance feedback is incorporated as a post-retrieval step for optimising the way evidence from different visual features is combined.  ...  Moreover, the assumption that users are always able to formulate an appropriate query is questionable.  ...  The possibility of altering the distance metric under relevance feedback is mentioned but not investigated in practice.  ... 
doi:10.1007/s11042-008-0207-2 fatcat:j5wp624byvhjznjxjp5f5frhry

Software Analytics in Practice

Dongmei Zhang, Shi Han, Yingnong Dang, Jian-Guang Lou, Haidong Zhang, Tao Xie
2013 IEEE Software  
approaches to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for completing various tasks around software systems, software users  ...  We also discuss the lessons learned from StackMine on applying software analytics technologies to make practice impact -solving problems that practitioners care about, using domain knowledge for correct  ...  The authors would like to thank the engineers from the Microsoft product teams for the collaboration on the StackMine project and all other collaboration projects with the Software Analytics group of Microsoft  ... 
doi:10.1109/ms.2013.94 fatcat:oobbdcos3jcopemg2awbegubj4

Comparing discriminating transformations and SVM for learning during multimedia retrieval

Xiang Sean Zhou, Thomas S. Huang
2001 Proceedings of the ninth ACM international conference on Multimedia - MULTIMEDIA '01  
Based on a careful examination of the problem and a detailed analysis of the existing solutions, we propose several discriminating transforms as the learning machine during the user interaction.  ...  On-line learning or "relevance feedback" techniques for multimedia information retrieval have been explored from many different points of view: from early heuristic-based feature weighting schemes to recently  ...  In this paper, we designate the learning task as the learning of a discriminating subspace from the limited number of examples provided by the user in an interactive fashion.  ... 
doi:10.1145/500160.500163 fatcat:w3ghiud5yzelxke43rqh6dvpni

Comparing discriminating transformations and SVM for learning during multimedia retrieval

Xiang Sean Zhou, Thomas S. Huang
2001 Proceedings of the ninth ACM international conference on Multimedia - MULTIMEDIA '01  
Based on a careful examination of the problem and a detailed analysis of the existing solutions, we propose several discriminating transforms as the learning machine during the user interaction.  ...  On-line learning or "relevance feedback" techniques for multimedia information retrieval have been explored from many different points of view: from early heuristic-based feature weighting schemes to recently  ...  In this paper, we designate the learning task as the learning of a discriminating subspace from the limited number of examples provided by the user in an interactive fashion.  ... 
doi:10.1145/500141.500163 fatcat:flk2vybznnhfdjqn3md3cg5xdm

A Unified Relevance Feedback Framework for Web Image Retrieval

En Cheng, Feng Jing, Lei Zhang
2009 IEEE Transactions on Image Processing  
Index Terms-Implicit feedback, relevance feedback (RF), search result clustering, web image retrieval.  ...  On the one hand, unlike traditional RF UI which enforces users to make explicit judgment on the results, the new UI regards the users' click-through data as implicit relevance feedback in order to release  ...  To strengthen our proposed framework, we employ implicit feedback to overcome the limitation of explicit feedback techniques where an increased cognitive burden is placed on the users.  ... 
doi:10.1109/tip.2009.2017128 pmid:19362910 fatcat:ygpc546pj5ftnbrgzsuxcillga

A Survey on Visual Content-Based Video Indexing and Retrieval

Weiming Hu, Nianhua Xie, Li Li, Xianglin Zeng, S. Maybank
2011 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback  ...  Its limitation is that it needs more user interaction, which requires more user patience and cooperation. 2) Implicit Relevance Feedback: This feedback refines retrieval results by utilizing click-through  ...  The limitation of implicit feedback is that the information gathered from the user is less accurate than in explicit feedback. 3) Pseudorelevance Feedback: This feedback selects positive and negative samples  ... 
doi:10.1109/tsmcc.2011.2109710 fatcat:qtenus4htffcfbyuiwidgjojku
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