Re-ranking by Multi-feature Fusion with Diffusion for Image Retrieval
Re-ranking by Multi-feature Fusion with Diffusion for Image Retrieval
复制标题
DOI：
10.1109/wacv.2015.82
复制
发表时间：
2015-01
期刊：
影响因子：
--
通讯作者：
Fan Yang;B. Matei;L. Davis
中科院分区：
文献类型：
--
作者：
Fan Yang;B. Matei;L. Davis
提出了一种融合多特征信息的图像检索重排序算法。我们利用图像之间的成对相似性分数来利用图像之间的潜在关系。来自每个特征的查询的初始排名列表被表示为无向图，其中边缘强度来自特定于特征的图像相似性。来自多个特征的图由混合马尔可夫模型组合。此外，我们利用一个概率模型的基础上相似和不相似的图像对的相似性分数的统计，以确定每个图的权重。特征的权重是查询特定的，其中不同查询的排名列表接收不同的权重。我们计算权重的方法是数据驱动的，不需要任何学习。一个扩散过程，然后应用到融合图，以减少噪声，实现更好的检索性能。实验表明，我们的方法显着提高了性能的基线方法，并优于许多国家的最先进的检索方法。
We present a re-ranking algorithm for image retrieval by fusing multi-feature information. We utilize pair wise similarity scores between images to exploit the underlying relationships among images. The initial ranked list for a query from each feature is represented as an undirected graph, where edge strength comes from feature-specific image similarity. Graphs from multiple features are combined by a mixture Markov model. In addition, we utilize a probabilistic model based on the statistics of similarity scores of similar and dissimilar image pairs to determine the weight for each graph. The weight for a feature is query specific, where the ranked lists of different queries receive different weights. Our approach for calculating weights is data-driven and does not require any learning. A diffusion process is then applied to the fused graph to reduce noise and achieve better retrieval performance. Experiments demonstrate that our approach significantly improves performance over baseline methods and outperforms many state-of-the-art retrieval methods.