JACIII Vol.22 No.2 pp. 280-289
doi: 10.20965/jaciii.2018.p0280


A Cross-Media Retrieval Algorithm Based on Consistency Preserving of Collaborative Representation

Fei Shang, Huaxiang Zhang, Jiande Sun, Li Liu, and Hui Zeng

Department of Computer Science, Shandong Normal University
No. 1, University Road, Changqing District, Jinan 250300, China

Corresponding author

September 18, 2017
February 1, 2018
March 20, 2018
cross-media retrieval, dictionary learning, collaborative representation, consistency preserving

Unlike traditional methods that directly map different modalities into an isomorphic subspace for cross-media retrieval, this paper proposes a cross-media retrieval algorithm based on the consistency of collaborative representation (called CR-CMR). In order to measure the similarity between data coming from different modalities, CR-CMR first takes the advantage of dictionary learning techniques to obtain homogeneous collaborative representation for texts and images, then, it considers the semantic consistency of different modalities simultaneously and maps the collaborative representation coefficients into an isomorphic semantic subspace to conduct cross-media retrieval. Experimental results on three state-of-the-art datasets show that the algorithm is effective.

Cite this article as:
F. Shang, H. Zhang, J. Sun, L. Liu, and H. Zeng, “A Cross-Media Retrieval Algorithm Based on Consistency Preserving of Collaborative Representation,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.2, pp. 280-289, 2018.
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