Abstract: For automatic object detection tasks, large amounts of training images are usually labeled to achieve more reliable training of the object classifiers; this is cost-expensive since it requires hiring professionals to label large-scale training images. When a large number of object classes come into view, the issue of obtaining a large enough amount of the labeled training images becomes more critical. There are three potential solutions to reduce the burden for image labeling: (1) allowing people to provide the object labels loosely at the image level rather than at the object level (e.g., loosely-tagged images without identifying the exact object locations in the images); (2) harnessing large-scale collaboratively-tagged images that are available on the Internet; and, (3) developing new machine learning algorithms that can directly leverage large-scale collaboratively- or loosely-tagged images for achieving more effective training of a large number of object classifiers. Based on these observations, a multi-task multi-label multiple instance learning (MTML-MIL) algorithm is developed in this paper by leveraging both inter-object correlations and large-scale loosely-labeled images for object classifier training. By seamlessly integrating multi-task learning, multi-label learning, and multiple instance learning, our MTML-MIL algorithm can achieve more accurate training of a large number of inter-related object classifiers (where an object network is constructed for determining the inter-related learning tasks directly in the feature space rather than in the label space). Our experimental results have shown that our MTML-MIL algorithm can achieve higher detection accuracy rates for automatic object detection.

Key words: Object network, Loosely tagged images, Multi-task learning, Multi-label learning, Multiple instance learning

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