Authors:
Steven Verstockt
;
Peter Lambert
and
Rik Van De Walle
Affiliation:
Ghent University, Belgium
Keyword(s):
Object recognition, Feature extraction, Localized CBIR, Query by selection, SIFT
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper addresses the problem of localized content based image retrieval. Contrary to classic CBIR systems which rely upon a global view of the image, localized CBIR only focuses on the portion of the image where the user is interested in, i.e. the relevant content. Using the proposed algorithm, it is possible to recognize an object by clicking on it. The algorithm starts with an automatic gamma correction and bilateral filtering. These pre-processing steps simplify the image segmentation. The segmentation itself uses dynamic region growing, starting from the click position. Contrary to the majority of segmentation techniques, region growing only focuses on that part of the image that contains the object. The remainder of the image is not investigated. This simplifies the recognition process, speeds up the segmentation, and increases the quality of the outcome. Following the region growing, the algorithm starts the recognition process, i.e., feature extraction and matching. Based
on our requirements and the reported robustness in many state-of-the-art papers, the Scale Invariant Feature Transform (SIFT) approach is used. Extensive experimentation of our algorithm on three different datasets achieved a retrieval efficiency of approximately 80%.
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