Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/62837
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCheng, Yin Hao
dc.date.accessioned2015-04-29T09:13:21Z
dc.date.available2015-04-29T09:13:21Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10356/62837
dc.description.abstractDigital content have been gaining more attention in recent years, and digital images are becoming more popular as a means of personal recount of people’s life. To deal with the sudden influx of digital images, there is a need for automatic image annotation to automatically categorize the images via their semantic content. This will allow people to maintain their digital images library easily, providing a user friendly platform for storage and sharing of personal memories. In this report, a 4-phrase methodology for image annotation was used. Each of the 4 phrases can be easily replaced or modified without affecting other phrases, much like the modular approach in software engineering design. Each of the phrases will target a specific area of the image annotation process.To achieve the best results, experimentations were done using the various extraction techniques in combination with the classification techniques. This allowed us to discover the technique or combination of techniques that will obtain the highest accuracy in terms of image annotation. The results yielded from the experimentations indicated that the best combination of techniques is the use of Dense SIFT (Level 1) with Bag of keypoints and classified via Linear Support Vector Machine (SVM). The relatively faster speed of this combination as compared with other technique combinations enable this technique combination to have the best of both worlds, namely accuracy and processing speed.en_US
dc.format.extent49 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.titleImage annotation by searchen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorXu Dongen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
FYP Report Cheng Yin Hao - Image Annotation By Search.pdf
  Restricted Access
Image Annotation by Search1.87 MBAdobe PDFView/Open

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.