Universiti Teknologi Malaysia Institutional Repository

Comparison Of Two Different Proposed Feature Vectors For Classification Of Complex Image

Zafar, Muhammad Faisal and Mohamad, Dzulkifli (2005) Comparison Of Two Different Proposed Feature Vectors For Classification Of Complex Image. Jurnal Teknologi D (42D). pp. 65-82. ISSN 0127-9696

[img] PDF
575Kb

Official URL: http://www.penerbit.utm.my/onlinejournal/42/D/JTJU...

Abstract

Many Applications Of Pattern Recognition Use A Set Of Local Features For Recognition Purpose. Instead Of Using Only Local Features, This Paper Presents A Method To Extract Features From Image Body Globally As Well. The System Takes Into Account Several Geometrical Effects Such As Area, Euclidean Distance Etc And Their Different Ratios. It Utilizes Thresholding And Region Extraction Methods For Gray Level Trademarks Images, Which Furnish These Images And Segment Their Separate Portions. Thus Both Local And Global Traits Are Constructed That Take Advantage Of The Pixel Statistics To Form A More Compact Representation Of The Image, While Maintaining Good Recognition Accuracies. Two Feature Vectors Have Been Proposed. These Feature Vectors Are Comprised Of Nine And Seven Constituents, Respectively. Formation Of Individual Features Is Very Simple Involving Uncomplicated Ratios Of Geometric And Numeric Estimate Of Images’ Pixels. The Vectors Designed Are Based On The Invariance Properties Of Individual Features. One Feature Vector Is Invariant To Rotation, Translation And Size, While The Other Has An Extra Invariance Regarding Scale. In Addition, A Comparative Study On Two Feature Sets Is Described Using Backpropagation Neural Network (BPN) As A Classifier. The Classification Results Are Encouraging Which Ranges From 74 To 94% For Different Data Sets.

Item Type:Article
Uncontrolled Keywords:Pattern Recognition, Trademark Matching, Feature Extraction, Segmentation, Backpropagation Neural Network
Subjects:Q Science > Q Science (General)
Divisions:Computer Science and Information System (Formerly known)
ID Code:1814
Deposited By: En Mohd. Nazir Md. Basri
Deposited On:19 Mar 2007 04:47
Last Modified:15 Oct 2010 04:32

Repository Staff Only: item control page