| Title | Hash Retrieval Method for Recaptured Images Based on Convolutional Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, J., Wang, X., Liu, S. |
| Conference Name | 2020 2nd World Symposium on Artificial Intelligence (WSAI) |
| Keywords | AD images, advertising, advertising data processing, Binary codes, binary hash code output, compositionality, Convolutional codes, convolutional neural network, convolutional neural networks, cryptography, feature extraction, hash algorithms, hash layer, hash retrieval method, hashing method, image retrieval, outdoor advertising market researching, pubcrawl, recaptured advertising images, recaptured image retrieval, Resiliency, retrieval performance, retrieving algorithm, Robustness, Training, visualization |
| Abstract | For the purpose of outdoor advertising market researching, AD images are recaptured and uploaded everyday for statistics. But the quality of the recaptured advertising images are often affected by conditions such as angle, distance, and light during the shooting process, which consequently reduce either the speed or the accuracy of the retrieving algorithm. In this paper, we proposed a hash retrieval method based on convolutional neural networks for recaptured images. The basic idea is to add a hash layer to the convolutional neural network and then extract the binary hash code output by the hash layer to perform image retrieval in lowdimensional Hamming space. Experimental results show that the retrieval performance is improved compared with the current commonly used hash retrieval methods. |
| DOI | 10.1109/WSAI49636.2020.9143281 |
| Citation Key | li_hash_2020 |
Hash Retrieval Method for Recaptured Images Based on Convolutional Neural Network