Content-based image retrieval using spatial-color and Gabor texture on a mobile device

Abstract

Mobile image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes a new efficient and effective mobile image retrieval method that applies a weighted combination of color and texture utilizing spatial-color and second order statistics. The system for mobile image searches runs in real-time on an iPhone and can easily be used to find a specific image. To evaluate the performance of the new method, we assessed the Xcode simulations performance in terms of average precision and F-score using several image databases and compare the results with those obtained using existing methods such as MPEG-7. Experimental trials revealed that the proposed descriptor exhibited a significant improvement of over 13% in retrieval effectiveness, compared to the best of the other descriptors.

Ir-Hf-Wed: Image Retrieval Using Hybrid Feature Extraction With Weighted Euclidean Distance

“…For big data applications, retrieval of data plays a vital role, however this process has to be accurate and efficient for the analysis to be performed on the retrieved data at latter phase. Thus, considering an application that is designed for image analysis, retrieval of images is a major task that is poised with challenges like real-time retrieval, feature extraction quality and retrieval cost [3].…”

Content Based Image Recognition by Information Fusion with Multiview Features

“…Content based feature extraction has become an active area of research in the last few decades with proliferation of digital images from various image capturing sources and the internet [1,40,41]. Extraction of meaningful information from image content has been well perceived in spatial domain and in frequency domain [2].…”