Efficient fine-texture image retrieval using deep multi-view hashing.

Fine-texture Image Retrieval, a special case in Content Based Image Retrieval, has high potential application value in many fields. However, there are very few researches focus on this special case. Due to the difference between fine-texture images and common images, general retrieval methods for common images are difficult to apply to fine-texture image retrieval. It is also a challenging issue with several obstacles: variety and complexity of appearance, as well as high requirements for retrieval accuracy. To address this issue, this study proposes a novel approach, called deep multi-view hashing (DMVH), to learn enhanced hash codes for efficient fine-texture image retrieval. We propose to use the first few layers of a deep convolutional neural network for fine-texture image presentation. To avoid overfitting, we employ L0Linear layer instead of the commonly used Linear layer in all the fully connected layers. Then we introduce a pairwise quantified similarity computed on the semantic labels. And the learning process of the model is guided by multiple labels of images. This study takes fabric as an example, and builds a dataset called MFT-fabric-v1, to validate the effectiveness of the DMVH method. On this data set, the proposed model achieved a performance with a mAP value of o.8735. The area under the pr curve is larger than other comparison methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art on the MFT-fabric-v1 dataset. [Display omitted] • A method for image retrieval with fine texture is proposed. • The proposed method describes the image from multiple views and uses it for retrieval. • An efficient end-to-end hashing method is proposed. [ABSTRACT FROM AUTHOR]