Abstract
AbstractImage retrieval via traditional Content-Based Image Retrieval (CBIR) often incurs the semantic gap problem—non-correlation of image retrieval results with human semantic interpretation of images. In this paper, Relevance Feedback (RF) mechanism was incorporated into a traditional Query by Visual Example CBIR (QVER) system. The inherent curse of dimensionality associated with RF mechanism was catered for by performing feature selection using Principal Component Analysis (PCA). The amount of feature dimension retained was determined based on a not more than 5% loss constrain imposed on average precision of retrieval result. While the asymmetry and small sample size nature of the resultant image dataset informed the use of a modified One-Class Support Vector Machine (OC-SVM) classifier, three image databases (DB10, DB20 and DB100) were used to test the OC-SVM RF mechanism. Across DB10, DB20 and DB100, Average Indexing Time of 0.451, 0.3017, and 0.0904s were recorded, respectively. For a critical reca...
Highlights
IntroductionDigital images come from diverse sources (such as digital cameras, handheld mobile devices, scanners, and medical imaging devices) and for different applications (ranging from personal photograph, entertainment, crime prevention and control, satellite imaging, medicine, to research and training)
Digital images come from diverse sources and for different applications
The mean precision increased from 0.3660 to 0.7660 for CM54; from 0.5680 to 0.6600 for GW48; from 0.3860 to 0.7920 for WM40; and from 0.3340 to 0.7420 for Hist. These results showed that there is an appreciable improvement on the mean precision with the application of the One-Class Support Vector Machine (OC-SVM) Relevance Feedback (RF)
Summary
Digital images come from diverse sources (such as digital cameras, handheld mobile devices, scanners, and medical imaging devices) and for different applications (ranging from personal photograph, entertainment, crime prevention and control, satellite imaging, medicine, to research and training). The volume of information stored in image format is increasing exponentially (Chung, 2007). Owing to the volume and the diversity of image databases, there is a need for effective and efficient means of retrieving specific images from the available dataset. The adoption of human experts to manually annotate images in large databases of generic image types can be very costly and laborious, highly subjective, and the interpretation of a particular image may be inconsistent under different scenarios. The visual content of digital images must be analyzed to ensure effective and efficient image retrieval
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
