Animal image retrieval algorithms based on deep neural network.
Yin Pei, M. M. Kamruzzaman · 2019
Nowadays, with the development of science and technology, image data has become an important part of network data, especially animal images. It is of great significance to effectively retrieve animal images from massive image data of network data for discovering animals or protecting animals. The traditional image retrieval algorithm has the insufficient ability in feature extraction, which is not suitable for effective network image retrieval. The application of depth neural network in image retrieval brings a breakthrough for solving this problem. Aiming at the problem of network animal image data retrieval, a hash network algorithm based on deep neural network is proposed. This method firstly extracts the high-level semantic features of animal images by using depth neural network, and optimizes the depth neural network. Then, the obtained image semantic features are input into the sparse self-coding hash network to obtain short hash codes. Finally, an image retrieval algorithm based on the weight of hash codes is proposed. The simulation results show that the improved depth neural network is used to extract the high-level semantic information of the image, and then the sparse self-coding hash network is used for further training. The obtained short hash codes are retrieved by using the image retrieval algorithm based on the weight of hash codes, which can not only realize animal image retrieval, but also improve the accuracy of image retrieval.