Flower Image Retrieval Based on Saliency Map
Xuelong Hu, Hui-Ning Wu, Yuhui Zhang, Lei Sun · 2014
Most of general content-based image retrieval (CBIR) algorithms cannot meet the demand for fine retrieval of flower images. Combing with features of flower images, this paper proposed a flower image retrieval algorithm based on saliency map. Firstly, to obtain the saliency map, the improved Itti's visual attention model was utilized, and then the color and LBP texture feature were extracted using the saliency map, so as to the image segmentation was avoided. Finally, the retrieval experiments on flower image data sets of the VGG group were finished. Comparative results show that the proposed algorithm is more effective than the other two algorithms, i.e. color histogram combined with LBP texture histogram based on the original image (CT), and color and LBP texture histogram based on the saliency map extracted by Itti model (ICT).