Deep objectness hashing using large weakly tagged photos

CNN-based hashing methods have greatly boosted the performance of image retrieval, under the strong supervision of large amounts of manually annotated labels. In recent years, a large number of social media images with user tags have been generated on the Internet. These images can be regarded as weakly labeled training data, which can provide rich samples for training hash network, and greatly reduce the cost of obtaining training data. However, there are noise and visual irrelevant tags in user tags, and different tags may describe different objects in the image. In the previous CNN-based hashing method, a training image usually corresponds to a manual label and generates a hash code. These methods are difficult to use the image described by user tags with noise. For solving the above problem, we propose a CNN-based objectness hash learning method using user tags as a guide for training. First of all, the user tags are roughly filtered to remove noise tags that are not related to the visual content of images. Secondly, we quantify user tags into a unified semantic space and extract the highest-frequency words of the semantic space from similar objectness areas as their labels. Then, these objectness areas with their labels are grouped into a series of triple units as training data. So that the generated hash code can inherit the semantic similarity of the objectness areas well, that is, the Hamming distance between hash codes generated by similar objectness areas is closer, the reverse is the farther. Experimental results on NUS-WIDE and Flickr datasets show that our method can effectively extract object-level semantic information from weak user tags, and improve the accuracy of image retrieval.