A multi-instance and multi-label learning method based on Content Based Image Retrieve ( CBIR) is proposed in this paper, and the image processing stage we use ...

Oct 18, 2016 · Abstract. Because multi-instance and multi-label learning can effectively deal with the problem of ambiguity when processing images.

We correspond the instances with category labels by using a package which contains the color and texture features of the image area. According to the user to ...

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with ...

The MIML (Multi- Instance Multi-Label learning) framework which is associated with multiple class labels for Image Annotation is proposed and it is ...

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with ...

Abstract Recently, a reasonable and effectively framework to deal with the classification problem of the polysemy object with complex connotation is ...

We adapt this framework to Multiple Instance Multiple Label learning by 1) designing a loss function that models the loss for the bag of instances and 2 ...

Missing: Image Retrieval.

A multi-instance learning based CBIR approach is presented that achieves comparable results to some existing approaches and is even more efficient.