000167916 001__ 167916
000167916 005__ 20180128032053.0
000167916 0247_ $$2doi$$a10.5075/epfl-thesis-5166
000167916 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis5166-0
000167916 02471 $$2nebis$$a6549719
000167916 037__ $$aTHESIS_LIB
000167916 041__ $$aeng
000167916 088__ $$a5166
000167916 245__ $$aObject Duplicate Detection
000167916 269__ $$a2011
000167916 260__ $$aLausanne$$bEPFL$$c2011
000167916 300__ $$a258
000167916 336__ $$aTheses
000167916 520__ $$aWith the technological evolution of digital acquisition and storage technologies, millions of images and video sequences are captured every day and shared in online services. One way of exploring this huge volume of images and videos is through searching a particular object depicted in images or videos by making use of object duplicate detection. Therefore, need of research on object duplicate detection is validated by several image and video retrieval applications, such as tag propagation, augmented reality, surveillance, mobile visual search, and television statistic measurement. Object duplicate detection is detecting visually same or very similar object to a query. Input is not restricted to an image, it can be several images from an object or even it can be a video. This dissertation describes the author's contribution to solve problems on object duplicate detection in computer vision. A novel graph-based approach is introduced for 2D and 3D object duplicate detection in still images. Graph model is used to represent the 3D spatial information of the object based on the local features extracted from training images so that an explicit and complex 3D object modeling is avoided. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Our method is shown to be robust in detecting the same objects even when images containing the objects are taken from very different viewpoints or distances. Furthermore, we apply our object duplicate detection method to video, where the training images are added iteratively to the video sequence in order to compensate for 3D view variations, illumination changes and partial occlusions. Finally, we show several mobile applications for object duplicate detection, such as object recognition based museum guide, money recognition or flower recognition. General object duplicate detection may fail to detection chess figures, however considering context, like chess board position and height of the chess figure, detection can be more accurate. We show that user interaction further improves image retrieval compared to pure content-based methods through a game, called Epitome.
000167916 6531_ $$aobject duplicate detection
000167916 6531_ $$aimage analysis
000167916 6531_ $$amobile visual search
000167916 6531_ $$agraph matching
000167916 700__ $$0242658$$aVajda, Péter$$g182052
000167916 720_2 $$0240223$$aEbrahimi, Touradj$$edir.$$g105043
000167916 8564_ $$iINTERNAL$$uhttps://infoscience.epfl.ch/record/167916/files/EPFL_TH5166.pdf$$xPUBLIC$$zTexte intégral / Full text
000167916 909C0 $$0252077$$pMMSPL
000167916 909CO $$ooai:infoscience.tind.io:167916$$pthesis-bn2018$$pthesis$$pSTI
000167916 918__ $$aSTI$$cIEL$$dEDIC2005-2015
000167916 919__ $$aGR-EB
000167916 920__ $$b2011
000167916 970__ $$a5166/THESES
000167916 973__ $$aEPFL$$sPUBLISHED
000167916 980__ $$aTHESIS