Self-adaptive Feature Extraction Scheme for Mobile Image Retrieval of Flowers
Khin Hninn Phyu, Andrea Kutics, Akihiko Nakagawa · 2012
This paper proposes a new self-adaptive feature extraction scheme to improve retrieval precision for Content-based Image Retrieval (CBIR) systems on mobile phones such that users can search similar pictures for a query image taken from their mobile phones. The proposed methods employ a newly modified extraction method using the Canny edge-based Edge Histogram Descriptor (CEHD), Color Layout Descriptor (CLD) and the Curvature Scale Space (CSS) shape-based descriptor. To obtain object shapes, salient regions are detected by means of a multi-scale self-developed segmentation model. Experiments were conducted using flower images as image data in order to verify the most pertinent feature extraction methods in designing a domain knowledge-driven self-adaptive feature extraction scheme. Test results prove that the CSS descriptor is useful to determine prominent features of a flower image before employing additional extraction techniques. By that means, the system can enhance retrieval precision and avoid unnecessarily extracting insignificant features.