Shape characterization using laws of texture energy measures facilitating retrieval

ABSTRACT

Feature characterization schemes catering shape indexing and retrieval have been a subject undergoing intense study in computer vision. Here, a feature characterization scheme is presented using the Laws of Texture Energy Measures targeting shape retrieval. The LTEM-based descriptor refines edges of shape images to produce highly discriminative features. Later, a feature representation arrangement packs it into global-structural shape histograms that are, subsequently, used for matching and retrieval. Exhaustive experiments of the resulting descriptor across the MPEG-7, Tari-1000 and Kimia’s 99 datasets render consistent Bull’s Eye Retrieval rate of 90%, revealing its highly distinctive nature among the intra- and inter-shape classes. Moreover, the witnessed BER clearly indicates that the descriptor is robust to different affine transformations.

KEYWORDS:

• Affine transformation
• Bull’s Eye Retrieval
• datasets
• image histogram
• laws of texture energy
• shape retrieval

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

P Govindaraj received the B.E. degree in Electronics and Communication engineering from VTU, Karnataka, India in 2013 and M.Tech. in Signal Processing from VTU, Karnataka in 2015. His current research interests include image hashing, image retrieval and computer vision.

M.S. Sudhakar received the B.E., M.E. and Ph.D. from Anna University, Tamil Nadu, India. Currently, he is an Associate professor with VIT University, India. His current research interests include image retrieval and computer vision. He is the author or co-author of more than 15 scientific papers in international conferences or journals.

ORCID

P. Govindaraj http://orcid.org/0000-0003-0788-8914