模式识别(心理学)
计算机科学
判别式
人工智能
图形
特征(语言学)
核(代数)
图形核
模式
特征提取
核方法
支持向量机
数学
理论计算机科学
多项式核
哲学
语言学
组合数学
社会科学
社会学
作者
Jiejun Xu,Vignesh Jagadeesh,B.S. Manjunath
标识
DOI:10.1109/tmm.2013.2291218
摘要
The problem of multi-label image classification using multiple feature modalities is considered in this work. Given a collection of images with partial labels, we first model the association between different feature modalities and the images labels. These associations are then propagated with a graph diffusion kernel to classify the unlabeled images. Towards this objective, a novel Fused Multimodal Bi-relational Graph representation is proposed, with multiple graphs corresponding to different feature modalities, and one graph corresponding to the image labels. Such a representation allows for effective exploitation of both feature complementariness and label correlation. This contrasts with previous work where these two factors are considered in isolation. Furthermore, we provide a solution to learn the weight for each image graph by estimating the discriminative power of the corresponding feature modality. Experimental results with our proposed method on two standard multi-label image datasets are very promising.
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