This paper is published in Volume-3, Issue-3, 2017
Area
Image Processing & Retrieval System
Author
Vaishnavi Gadewar
Co-authors
Apoorva Deshmukh, Akshay Ekurge, Ashok Shinde
Org/Univ
International Institute Of Information Technology, Pune, Maharashtra, India
Pub. Date
06 June, 2017
Paper ID
V3I3-1463
Publisher
Keywords
Texture, Feature Vector, Feature Extraction, Distance Matrices, Precision & Recall

Citationsacebook

IEEE
Vaishnavi Gadewar, Apoorva Deshmukh, Akshay Ekurge, Ashok Shinde. Classification and Retrieval of Texture Images Using Gabor Filtering and Statistical Features, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Vaishnavi Gadewar, Apoorva Deshmukh, Akshay Ekurge, Ashok Shinde (2017). Classification and Retrieval of Texture Images Using Gabor Filtering and Statistical Features. International Journal of Advance Research, Ideas and Innovations in Technology, 3(3) www.IJARIIT.com.

MLA
Vaishnavi Gadewar, Apoorva Deshmukh, Akshay Ekurge, Ashok Shinde. "Classification and Retrieval of Texture Images Using Gabor Filtering and Statistical Features." International Journal of Advance Research, Ideas and Innovations in Technology 3.3 (2017). www.IJARIIT.com.

Abstract

Content Based Image Retrieval (CBIR) is a developing trend in Digital Image processing. CBIR is used to search and retrieve the query image from wide range of database. Many features and algorithms can be used for efficient image retrieval. The features of CBIR as colors, shapes, textures etc. can be derived from image itself. This project represents the retrieval of images based on the texture images. The focus of project is on the images processing aspects and particular using texture features. With the help of Gabor filter features and some statistical feature of texture images we form a feature vector for extraction of features. For retrieval of images these features are useful. The most accurate result is obtained using Gabor features applied on Brodatz texture database. For comparing and finding similarity between features of query image and another image in the database is done by using Euclidean distance, Vector cosine angle distance and Manhattan distance. To analyze the better performance of retrieval of images these distances we plot the precision and recall graph with number of retrieved images respectively.
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