基于潜语义主题加强的跨媒体检索算法

针对不同模态数据对相同语义主题表达存在差异性，以及传统跨媒体检索算法忽略了不同模态数据能以合作的方式探索数据的内在语义信息等问题，提出了一种新的基于潜语义主题加强的跨媒体检索（LSTR）算法。首先，利用隐狄利克雷分布（LDA）模型构造文本语义空间，然后以词袋（BoW）模型来表达文本对应的图像；其次，使用多分类逻辑回归对图像和文本分类，用得到的基于多分类的后验概率表示文本和图像的潜语义主题；最后，利用文本潜语义主题去正则化图像的潜语义主题，使图像的潜语义主题得到加强，同时使它们之间的语义关联最大化。在Wikipedia数据集上，文本检索图像和图像检索文本的平均查准率为57.0%，比典型相关性分析（CCA）、SM（Semantic Matching）、SCM（Semantic Correlation Matching）算法的平均查准率分别提高了35.1%、34.8%、32.1%。实验结果表明LSTR算法能有效地提高跨媒体检索的平均查准率。

Cross-media retrieval based on latent semantic topic reinforce

As an important and challenging problem in the multimedia area, common semantic topic has different expression across different modalities, and exploring the intrinsic semantic information from different modalities in a collaborative manner was usually neglected by traditional cross-media retrieval methods. To address this problem, a Latent Semantic Topic Reinforce cross-media retrieval (LSTR) method was proposed. Firstly, the text semantic was represented based on Latent Dirichlet Allocation (LDA) and the corresponding images were represented with Bag of Words (BoW) model. Secondly, multiclass logistic regression was used to classify both texts and images, and the posterior probability under the learned classifiers was exploited to indicate the latent semantic topic of images and texts. Finally, the learned posterior probability was used to regularize their image counterparts to reinforce the image semantic topics, which greatly improved the semantic similarity between them. In the Wikipedia data set, the mean Average Precision (mAP) of retrieving text with image and retrieving image with text is 57.0%, which is 35.1%, 34.8% and 32.1% higher than that of the Canonical Correlation Analysis (CCA), Semantic Matching (SM) and Semantic Correlation Matching (SCM) method respectively. Experimental results show that the proposed method can effectively improve the average precision of cross-media retrieval.