Salient Object Subitizing
We study the problem of salient object subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1–4). To this end, we...
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| Veröffentlicht in: | International journal of computer vision 2017-09, Vol.124 (2), p.169-186 |
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| container_title | International journal of computer vision |
| container_volume | 124 |
| creator | Zhang, Jianming Ma, Shugao Sameki, Mehrnoosh Sclaroff, Stan Betke, Margrit Lin, Zhe Shen, Xiaohui Price, Brian Měch, Radomír |
| description | We study the problem of salient object subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1–4). To this end, we present a salient object subitizing image dataset of about 14 K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained convolutional neural network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval. |
| doi_str_mv | 10.1007/s11263-017-1011-0 |
| format | Article |
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This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1–4). To this end, we present a salient object subitizing image dataset of about 14 K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained convolutional neural network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.</description><identifier>ISSN: 0920-5691</identifier><identifier>EISSN: 1573-1405</identifier><identifier>DOI: 10.1007/s11263-017-1011-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Analysis ; Artificial Intelligence ; Artificial neural networks ; Computer Imaging ; Computer Science ; Cues ; Human performance ; Image detection ; Image Processing and Computer Vision ; Image retrieval ; Mathematical models ; Neural networks ; Object recognition ; Pattern Recognition ; Pattern Recognition and Graphics ; Predictions ; Salience ; Vision ; Vision systems</subject><ispartof>International journal of computer vision, 2017-09, Vol.124 (2), p.169-186</ispartof><rights>Springer Science+Business Media New York 2017</rights><rights>COPYRIGHT 2017 Springer</rights><rights>International Journal of Computer Vision is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-548af4f37a63d84cfcfe3be85b3b63cbcbbef90ecf8f719097cc57a2ebddd0b43</citedby><cites>FETCH-LOGICAL-c389t-548af4f37a63d84cfcfe3be85b3b63cbcbbef90ecf8f719097cc57a2ebddd0b43</cites><orcidid>0000-0002-9954-6294 ; 0000-0002-0711-4313</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11263-017-1011-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11263-017-1011-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Zhang, Jianming</creatorcontrib><creatorcontrib>Ma, Shugao</creatorcontrib><creatorcontrib>Sameki, Mehrnoosh</creatorcontrib><creatorcontrib>Sclaroff, Stan</creatorcontrib><creatorcontrib>Betke, Margrit</creatorcontrib><creatorcontrib>Lin, Zhe</creatorcontrib><creatorcontrib>Shen, Xiaohui</creatorcontrib><creatorcontrib>Price, Brian</creatorcontrib><creatorcontrib>Měch, Radomír</creatorcontrib><title>Salient Object Subitizing</title><title>International journal of computer vision</title><addtitle>Int J Comput Vis</addtitle><description>We study the problem of salient object subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1–4). To this end, we present a salient object subitizing image dataset of about 14 K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained convolutional neural network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.</description><subject>Analysis</subject><subject>Artificial Intelligence</subject><subject>Artificial neural networks</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Cues</subject><subject>Human performance</subject><subject>Image detection</subject><subject>Image Processing and Computer Vision</subject><subject>Image retrieval</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>Pattern Recognition</subject><subject>Pattern Recognition and Graphics</subject><subject>Predictions</subject><subject>Salience</subject><subject>Vision</subject><subject>Vision 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Object Subitizing</title><author>Zhang, Jianming ; Ma, Shugao ; Sameki, Mehrnoosh ; Sclaroff, Stan ; Betke, Margrit ; Lin, Zhe ; Shen, Xiaohui ; Price, Brian ; Měch, Radomír</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-548af4f37a63d84cfcfe3be85b3b63cbcbbef90ecf8f719097cc57a2ebddd0b43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Analysis</topic><topic>Artificial Intelligence</topic><topic>Artificial neural networks</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Cues</topic><topic>Human performance</topic><topic>Image detection</topic><topic>Image Processing and Computer Vision</topic><topic>Image retrieval</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Object recognition</topic><topic>Pattern Recognition</topic><topic>Pattern Recognition and Graphics</topic><topic>Predictions</topic><topic>Salience</topic><topic>Vision</topic><topic>Vision systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Jianming</creatorcontrib><creatorcontrib>Ma, Shugao</creatorcontrib><creatorcontrib>Sameki, Mehrnoosh</creatorcontrib><creatorcontrib>Sclaroff, Stan</creatorcontrib><creatorcontrib>Betke, Margrit</creatorcontrib><creatorcontrib>Lin, Zhe</creatorcontrib><creatorcontrib>Shen, Xiaohui</creatorcontrib><creatorcontrib>Price, Brian</creatorcontrib><creatorcontrib>Měch, Radomír</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM 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This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1–4). To this end, we present a salient object subitizing image dataset of about 14 K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained convolutional neural network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11263-017-1011-0</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-9954-6294</orcidid><orcidid>https://orcid.org/0000-0002-0711-4313</orcidid></addata></record> |
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| ispartof | International journal of computer vision, 2017-09, Vol.124 (2), p.169-186 |
| issn | 0920-5691 1573-1405 |
| language | eng |
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| source | SpringerNature Journals |
| subjects | Analysis Artificial Intelligence Artificial neural networks Computer Imaging Computer Science Cues Human performance Image detection Image Processing and Computer Vision Image retrieval Mathematical models Neural networks Object recognition Pattern Recognition Pattern Recognition and Graphics Predictions Salience Vision Vision systems |
| title | Salient Object Subitizing |
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