Influence of the amount of context learned for improving object classification when simultaneously learning object and contextual cues.
Humans use visual context to improve object recognition. Yet, many machine vision algorithms still focus on local object features, discarding surrounding features as unwanted clutter. Here we study the impact of learning contextual cues while training an object classifier. In a new image database wi...
| Publicado en: | Visual Cognition Vol. 20; no. 4/5; pp. 580 - 603 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=75527967&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 75527967 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13506285 BE5 jtl: Visual Cognition issn: 13506285 maglogo: N pubinfo: dt: Apr2012 vid: 20 iid: 4/5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 75527967 10.1080/13506285.2012.667007 75527967 ppf: 580 ppct: 23 formats: tig: atl: Influence of the amount of context learned for improving object classification when simultaneously learning object and contextual cues. aug: au: Marat, Sophie Itti, Laurent affil: Department of Computer Science, University of Southern California, Los Angeles, CA, USA sug: ab: Humans use visual context to improve object recognition. Yet, many machine vision algorithms still focus on local object features, discarding surrounding features as unwanted clutter. Here we study the impact of learning contextual cues while training an object classifier. In a new image database with 10 object categories and 28,800 images, objects were presented in contextual or uniform backgrounds. Both the fraction of contextual backgrounds during training and the spatial extent of context were analysed. Local object features and broader context features were extracted by two biologically inspired algorithms, previously used for object and scene classification, respectively: HMAX, applied to a tight window around every object, and a “Gist” algorithm, applied to a larger yet still localized window. The descriptors from both algorithms were combined and processed by a Support Vector Machine. The recognition rate increased from 29%, without contextual cues, to 43% for objects presented in their context. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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