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Mostafa Charmi

Morteza Moradi, Farhad Bayat, Mostafa Charmi
Concept-Aware Web Image Compression Based on Crowdsourced Salient Object Detection

Reduced  output  quality  and  being  unaware  of content  are  among  major  issues  with  traditional  image compression techniques. Such issues cause some critical problems when  it  comes  to  quality-intensive  applications,  including object/face  detection and recognition,  Web-based image viewers and  management  systems,  etc.  On  the  other  side,  efficiency  of Web-based  image search engines and retrieval systems in terms of  user  experience  and  usability  could  be  affected.  In  order  to cope with these challenges, a novel image compression method is proposed  that  takes  advantages  of  collective  human  cognitive intelligence to detect the salient object(s) based on  the recognized key concept(s). Then, other less-important regions/objects will be subject  to  the  safe  compression.  Such  an  approach,  besides preserving  semantic  aspects  of  the  images  that  will  result  in smart  (concept-aware)  compression,  could  provide  some crowdsourced  labels  for  more  efficient  indexing  and  annotating of  images.  In  this  regard,  two  birds  could  be  beaten  with  one stone:  compressing  Web  images  with  respect  to  their content/concept  and  annotating  them  with  crowd-suggested labels.  The  experimental  results  as  well  as  user  acceptance evaluation proved the efficacy of the introduced method.



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