Hashing is an effective means for large-scale image retrieval. Preserving the semantic similarity in hash codes (i.e.

the distance between the hash codes of two images)should be small when the images are similar to improve the retrieval performance. Conventional methods first extract the overall image feature and then generate a single hash code. Such methods cannot characterize the image content for multiple objects

which results in a low accuracy of multi-label image retrieval. This study proposes a new hash generation method with object proposals. We propose a new deep-network-based framework to construct hash functions that learn directly from images that contain multiple labels. The model first derives a series of interesting regions that may contain objects and then generates the features of each region through deep convolutional neural networks. It finally generates a group of hash codes to describe all the objects in an image. The compact hash code will be generated to represent the entire image. A novel triplet-loss based training method is adopted to preserve the semantic order of the hash codes. The image retrieval experiments on the VOC2012

Flickr25K

and NUSWIDE datasets show that the NDCG (normalized discounted cumulative gain)value of our method can be improved by 2% to 4% unlike DSRH (deep semantic ranking hashing)and 3% to 6% unlike ITQ-CCA (iterative quantization-canonical correlation analysis)on VOC2012. Our method can attain the improvements by approximately 2% on Flickr25 and 4% on NUSWIDE. Our method can obtain 2% to 5% on the Flickr25 and NUSWIDE datasets over the DSRH for the map evaluation. Thus

the new method can describe an image accurately in a fine-grained way

and the performance is improved significantly for multi-label image retrieval. This study proposes a new model to learn compact features

and experiment results show that the fine-grained feature embedding of an image is practicable. Thus

our method outperforms other state-of-the-art hashing methods in terms of image retrieval.