Name
Abstract | ||
|---|---|---|
Cross-media retrieval is a challenging problem in multimedia retrieval area. In the real-world, many applications involve multi-modal data, e.g., web pages containing both images and texts. How to utilize the intrinsic intra-modality and inter-modality similarity to learn the appropriate relationships of the data objects and provide efficient search across different modalities is the core of cross-media retrieval. Inspired by the fact that hashing methods well address the fast retrieval problem in the large-scale data settings, designing a cross-media hashing approach which can perform efficient retrieval over heterogenous high-dimensional feature spaces is highly desirable. In this paper, we propose a cross-media hashing approach based on kernel regression (abbreviated as KRCMH) to obtain the hash codes for the data objects across different modalities. The experiments on two real-world data sets show that KRCMH achieves superior cross-media retrieval performance comparing with the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICME.2014.6890264 | ICME |
Keywords | Field | DocType |
intrinsic intramodality,cryptography,regression analysis,intermodality similarity,kernel regression,cross-media,heterogenous high-dimensional feature spaces,hash codes,multimedia retrieval area,hashing,cross-media hashing approach,image retrieval,cross-media retrieval,large-scale data settings,training data,correlation,internet,linear programming,kernel | Kernel (linear algebra),Locality-sensitive hashing,Data set,Web page,Pattern recognition,Computer science,Universal hashing,Feature hashing,Hash function,Artificial intelligence,Kernel regression,Machine learning | Conference |
ISSN | Citations | PageRank |
1945-7871 | 2 | 0.38 |
References | Authors | |
7 | 6 | |