Details

Autor(en) / Beteiligte

• Jianwei Luo
• Zhiguo Jiang

Titel

Learning Semantic Binary Codes by Encoding Attributes for Image Retrieval

Ist Teil von

• 2014 22nd International Conference on Pattern Recognition, 2014, p.279-284

Ort / Verlag

IEEE

Erscheinungsjahr

2014

Quelle

IEEE Xplore / Electronic Library Online (IEL)

Beschreibungen/Notizen

• This paper addresses the problem of learning semantic compact binary codes for efficient retrieval in large-scale image collections. Our contributions are three-fold. Firstly, we introduce semantic codes, of which each bit corresponds to an attribute that describes a property of an object (e.g. dogs have furry). Secondly, we propose to use matrix factorization (MF) to learn the semantic codes by encoding attributes. Unlike traditional PCA-based encoding methods which quantize data into orthogonal bases, MF assumes no constraints on bases, and this scheme is coincided with that attributes are correlated. Finally, to augment semantic codes, MF is extended to encode extra non-semantic codes to preserve similarity in origin data space. Evaluations on a-Pascal dataset show that our method is comparable to the state-of-the-art when using Euclidean distance as ground truth, and even outperforms state-of-the-art when using class label as ground truth. Furthermore, in experiments, our method can retrieve images that share the same semantic properties with the query image, which can be used to other vision tasks, e.g. re-training classifiers.