Tehnički vjesnik, Vol. 26 No. 6, 2019.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20190730110003
Cross-Media Semantic Matching based on Sparse Representation
Gongwen Xu*
; Business School, Shandong Jianzhu University, Jinan 250101, China
Aidong Zhai
; Maternity and Child Health Care Hospital, Zibo 255029, China
Jing Wang
; Business School, Shandong Jianzhu University, Jinan 250101, China
Zhijun Zhang
; Computer Science and Technology School, Shandong Jianzhu University, Jinan 250101, China
Xiaomei Li
; The Second Hospital, Shandong University, Jinan 250013, China
Sažetak
With the rapid growth of multi-modal data, cross-media retrieval has aroused many research interests. In this paper, the cross-media retrieval includes two tasks: query image retrieves relevant text and query text retrieves relevant images. With the development of sparse representation, two independent sparse representation classifiers are used to map the heterogeneous features of images and texts into their common semantic space before implementing similarity comparison. The proposed method makes full use of semantic information, and it is effective in the retrieving task. The performance of this method was evaluated on Wiki dataset, NUS-WIDE dataset, Wiki dataset with CNN features and Pascal dataset with CNN features. The experimental results validate its effectiveness compared with several state-of-the-art algorithms on the Mean Average Precision and other performance indexes.
Ključne riječi
cross-media retrieval; semantic matching; sparse representation
Hrčak ID:
228519
URI
Datum izdavanja:
27.11.2019.
Posjeta: 1.116 *