Sunday 19th of November 2017
 

Accurate Image Search using Local Descriptors into a Compact Image Representation


Soumia Benkrama, Lynda Zaoui and Christophe Charrier

Progress in image retrieval by using low-level features, such as colors, textures and shapes, the performance is still unsatisfied as there are existing gaps between low-level features and high-level semantic concepts. In this work, we present an improved implementation for the bag of visual words approach. We propose a image retrieval system based on bag-of-features (BoF) model by using scale invariant feature transform (SIFT) and speeded up robust features (SURF). In literature SIFT and SURF give of good results. Based on this observation, we decide to use a bag-of-features approach over quaternion zernike moments (QZM). We compare the results of SIFT and SURF with those of QZM. We propose an indexing method for content based search task that aims to retrieve collection of images and returns a ranked list of objects in response to a query image. Experimental results with the Coil-100 and corel-1000 image database, demonstrate that QZM produces a better performance than known representations (SIFT and SURF).

Keywords: Content-Based Image Retrieval Systems, feature detection, Bag of visual words.

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ABOUT THE AUTHORS

Soumia Benkrama
University of Science and Techenology Mohamed Boudiaf, Department of Computer Science, Laboratory Systems Signals Data

Lynda Zaoui
University of Science and Techenology Mohamed Boudiaf, Department of Computer Science, Laboratory Systems Signals Data

Christophe Charrier
University of Caen-Basse Normandie, GREYC, UMR CNRS 6072


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