A Novel Fish Image Retrieval Method Based on Saliency Spatial Pyramid

There are some characteristics about fish images, such as a wide variety of fish species, sheer numbers and difficult to retrieve. In this paper, we propose a fish image retrieval method based on saliency region and spatial pyramid. Firstly, we extract the interesting-regions of fish image by using saliency algorithm to reduce the influence of background in the images. And then, we extract SURF features from head, body and tail of fish respectively based on spatial pyramid to enhance the precision. At last, the similarity measurement method based on the proposed extracted features is given. In order to verify the robustness and effectiveness of the proposed method, we conduct experiments on QUT_fish_data and DLOU_fish_data datasets, the experimental results show that the method has higher recall and precision.

[1]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[2]  Thomas Deselaers,et al.  What is an object? , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Xinnian Wang,et al.  Boosting image retrieval framework with salient objects , 2016, 2016 International Conference on Audio, Language and Image Processing (ICALIP).

[4]  Donghua Zhou,et al.  Saliency detection and edge feature matching approach for crater extraction , 2015 .

[5]  Zhiyang Li,et al.  A new bag-of-words model using multi-cue integration for image retrieval , 2016, Int. J. Comput. Sci. Eng..

[6]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[7]  K. Hemachandran,et al.  Content Based Image Retrieval using Color and Texture , 2012 .

[8]  Ning Dai,et al.  Registration and integration algorithm in structured light three-dimensional scanning based on scale-invariant feature matching of multi-source images , 2012 .

[9]  Peter I. Corke,et al.  Local inter-session variability modelling for object classification , 2014, IEEE Winter Conference on Applications of Computer Vision.

[10]  Changshui Zhang,et al.  DeepFish: Accurate underwater live fish recognition with a deep architecture , 2016, Neurocomputing.

[11]  Hui Zhang,et al.  Local image representations using pruned salient points with applications to CBIR , 2006, MM '06.

[12]  Jenq-Neng Hwang,et al.  Supervised and Unsupervised Feature Extraction Methods for Underwater Fish Species Recognition , 2014, 2014 ICPR Workshop on Computer Vision for Analysis of Underwater Imagery.

[13]  Jenq-Neng Hwang,et al.  Recognizing live fish species by hierarchical partial classification based on the exponential benefit , 2014, 2014 IEEE International Conference on Image Processing (ICIP).