کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6958036 1451936 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Modeling intra- and inter-pair correlation via heterogeneous high-order preserving for cross-modal retrieval
ترجمه فارسی عنوان
مدلسازی همبستگی درونی و بین دو جفت از طریق نگهداری ناهمگن و مرتبه بالا برای بازیابی متقابل
کلمات کلیدی
بازیابی متقابل، حفظ نظم ناهمگن بالا، یادگیری همبستگی، هسته،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی
Cross modal (e.g., text-to-image or image-to-text) retrieval has received great attention with the flushed multi-modal social media data. It is of considerable challenge to stride across the heterogeneous gap between modalities. Existing methods project different modalities into a common space by minimizing the distance within the heterogeneous pairs (intra-pair) of the new latent space. However, the relationship among these multi-modal pairs (inter-pair) are neglected, which are beneficial to eliminate the heterogeneity. In this paper, we propose a novel algorithm based on canonical correlation analysis by considering the high-order relationship among pairs (HCCA) for cross-modal retrieval. Supervised with additional semantic labels and unsupervised without semantic labels are simultaneously considered by treating the intra- and inter-pair correlation discriminatively. Moreover, kernel tricks are also performed on HCCA to learn a non-linear projection, termed HKCCA. Extensive experiments conducted on three public datasets demonstrate the superiority of the proposed methods compared with the state-of-the-art approaches in cross modal retrieval.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 131, February 2017, Pages 249-260
نویسندگان
, , , ,