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中文摘要：
真实环境下数据集中广泛存在着标签噪声问题，数据集的弱标签性已严重阻碍了图像语义标注的实用化进程．针对弱标签数据集中的标签不准确、不完整和语义分布失衡现象，提出了一种适用于弱标签数据集的图像语义标注方法．首先，在视觉内容与标签语义的一致性约束、标签相关性约束和语义稀疏性约束下，通过直推式学习填充样本标签，构建样本的近似语义平衡邻域．鉴于邻域中存在噪声干扰，通过多标签语义嵌入的邻域最大边际学习获得距离测度和图像语义的一致性，使得近邻处于同一语义子空间．然后，以近邻为局部坐标基，通过邻域非负稀疏编码获得目标图像和近邻的部分相关性，并构建局部语义一致邻域．以邻域内的语义近邻为指导并结合语境相关信息，进行迭代式降噪与标签预测．实验结果表明了方法的有效性．
英文摘要：
Automatic semantic annotation, which automatically annotates images with semantic labels has received much research interest. Although it has been studied for years, image annotation is still far from practical. The effectiveness of traditional image annotation techniques heavily relies on the availability of a sufficiently large set of correct, complete and balanced labeled samples, which typically come from users in an interactive manual process. However, in real world environment, image labels are often incomplete, noisy and imbalanced. This paper investigates the usefulness of weakly labeled information and proposes an image annotation method for weakly labeled dataset. First, the missing labels are automatically filled by a transductive method which incorporates label correlation and semantic sparsity, along with the consistency of visual and semantic similarity. Then approximate semantic balanced neighborhood is constructed. A distance metric learning method for large margin nearest neighbor embedded in multiple labels is supplied, making the retrieved neighbors by this metric appear in the same semantic subspace. Local semantic consistent neighborhood is obtained by local nonnegative sparse coding. Meanwhile, an iterative denoising method for label inference is proposed to simultaneously handle the noise and annotate images under the guidance of semantic nearest neighbors and contextual information. Experimental results demonstrate the effectiveness and capability of the proposed method.
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