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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Joint Image Clustering and Labeling by Matrix Factorization
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Joint Image Clustering and Labeling by Matrix Factorization

机译：联合图像聚类和矩阵分解标注

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摘要

We propose a novel algorithm to cluster and annotate a set of input images jointly, where the images are clustered into several discriminative groups and each group is identified with representative labels automatically. For these purposes, each input image is first represented by a distribution of candidate labels based on its similarity to images in a labeled reference image database. A set of these label-based representations are then refined collectively through a non-negative matrix factorization with sparsity and orthogonality constraints; the refined representations are employed to cluster and annotate the input images jointly. The proposed approach demonstrates performance improvements in image clustering over existing techniques, and illustrates competitive image labeling accuracy in both quantitative and qualitative evaluation. In addition, we extend our joint clustering and labeling framework to solving the weakly-supervised image classification problem and obtain promising results.
机译：我们提出了一种新颖的算法来对一组输入图像进行聚类和批注，其中，图像被聚类为几个判别组，并且每个组都自动用代表性标签进行标识。为此，每个输入图像首先由候选标签的分布表示，基于其与已标记参考图像数据库中图像的相似性。然后，通过具有稀疏性和正交性约束的非负矩阵分解对一组这些基于标签的表示形式进行统一优化。改进的表示被用来对输入图像进行聚类和注释。所提出的方法展示了在图像聚类方面优于现有技术的性能，并展示了在定量和定性评估中具有竞争力的图像标记准确性。此外，我们扩展了联合聚类和标签框架，以解决弱监督图像分类问题并获得可喜的结果。

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