Content-Based Image Retrieval system for marine life images using gradient vector flow

Content Based Image Retrieval (CBIR) has been an active and fast growing research area in both image processing and data mining. Malaysia has been recognized with a rich marine ecosystem. Challenges of these images are low resolution, translation, and transformation invariant. In this paper, we have designed an automated CBIR system to characterize the species for future research. Gradient vector flow (GVF) has been implemented in a lot of image processing applications. Inspired by its fast image restoration algorithms we applied GVF for marine images. We evaluated different automated segmentation techniques and found GVF showing better retrieval results compared to other automated segmentation techniques.

[1]  Scott T. Acton,et al.  Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours , 2004, IEEE Transactions on Medical Imaging.

[2]  Konstantinos N. Plataniotis,et al.  Retrieval of images from artistic repositories using a decision fusion framework , 2004, IEEE Transactions on Image Processing.

[3]  Aly A. Farag,et al.  Variational Curve Skeletons Using Gradient Vector Flow , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Kiyoharu Aizawa,et al.  Advances in Multimedia Information Processing - PCM 2004, 5th Pacific Rim Conference on Multimedia, Tokyo, Japan, November 30 - December 3, 2004, Proceedings, Part I , 2005, Pacific Rim Conference on Multimedia.

[5]  Euripides G. M. Petrakis,et al.  Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Sean White,et al.  Searching the World's Herbaria: A System for Visual Identification of Plant Species , 2008, ECCV.

[7]  Hongchuan Yu,et al.  GVF-based anisotropic diffusion models , 2006, IEEE Transactions on Image Processing.

[8]  Gözde B. Ünal,et al.  Plant image retrieval using color and texture features , 2009, 2009 24th International Symposium on Computer and Information Sciences.

[9]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[10]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[11]  Ahsan Raza Sheikh,et al.  Automated Segmentation in Content Based Image Retrieval System for Marine Life Images Using Shape Feature , 2013 .

[12]  Nikos Paragios,et al.  Gradient vector flow fast geometric active contours , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Paul F. Whelan,et al.  A new GVF-based image enhancement formulation for use in the presence of mixed noise , 2010, Pattern Recognit..

[14]  Tae-Yong Kim,et al.  Shape-Based Image Retrieval Using Invariant Features , 2004, PCM.

[15]  Ahsan Raza Sheikh,et al.  A content based image retrieval system for marine life images , 2011, 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE).

[16]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[17]  André Ricardo Backes,et al.  Medical image retrieval based on complexity analysis , 2010, Machine Vision and Applications.

[18]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.