Title: Image retrieval using a scale-invariant feature transform bag-of-features model with salient object detection
Authors: Yih-Chearng Shiue; Sheng-Hung Lo; Yi-Cheng Tian; Cheng-Wei Lin
Addresses: Department of Information Management, National Central University, No.300, Zhongda Rd., Zhongli Dist., Taoyuan City, Taiwan ' Department of Information Management, National Central University, No.300, Zhongda Rd., Zhongli Dist., Taoyuan City, Taiwan ' Center for General Education, Hsin Sheng College of Medical Care and Management, No.418, Sec. Gaoping, Zhongfeng Rd., Longtan Dist., Taoyuan City, Taiwan ' Department of Information Management, National Central University, No.300, Zhongda Rd., Zhongli Dist., Taoyuan City, Taiwan
Abstract: How to effectively retrieve digital images is a focus of image retrieval research. Developed in the 1990s, content-based image retrieval (CBIR) systems are used to extract low-level visual features. However, semantic gaps exist between these features and high-level semantic concepts. This study proposes an image retrieval solution based on a bag-of-features (BoF) model integrated with scale-invariant feature transform (SIFT) and salient object detection. An image search system based on this image retrieval solution, which used object images as the query image, was subsequently constructed. Overall, the results verify the feasibility of the object-based image retrieval solution. Finally, the enhanced image search method and precision enabled constructing an image search system. The system is expected to improve through the search pattern, as well as improve the accuracy of images search, images search system to make a real attempt to solve the huge amount of data and images search difficult problems arising.
Keywords: image retrieval; content-based image retrieval; scale-invariant feature transform; bag-of-features model; k-means clustering.
International Journal of Applied Systemic Studies, 2017 Vol.7 No.1/2/3, pp.92 - 116
Available online: 18 Dec 2017Full-text access for editors Access for subscribers Free access Comment on this article