A Survey on Image Feature Descriptors

Automatically assigning relevant text keywords to image is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon many other fields but properties of features have not been well investigated. In most cases, a group of features is selected in advance but important feature properties are not well used to feature selection. In this paper the performance of different features are compared, different combinations of features and a number of classification methods applied on the image annotation task, which gives insight into the features properties are also discussed. General Terms Computer vision, Image Processing.

[1]  Clark N. Taylor,et al.  IEEE Transactions on Circuits and Systems for Video Technology information for authors , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  S TorresRicardo da,et al.  Comparative study of global color and texture descriptors for web image retrieval , 2012 .

[3]  L Chandrika Implementation Image Retrieval and Classification with SURF Technique , 2014 .

[4]  Yang Yu,et al.  Automatic image annotation using group sparsity , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Eizan Miyamoto,et al.  FAST CALCULATION OF HARALICK TEXTURE FEATURES , 2005 .

[7]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[8]  Thomas Sikora,et al.  The MPEG-7 visual standard for content description-an overview , 2001, IEEE Trans. Circuits Syst. Video Technol..

[9]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  Vincent Lepetit,et al.  Keypoint Signatures for Fast Learning and Recognition , 2008, ECCV.

[11]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[12]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.