A Mechanism for Sketch Based Image Retrievals using Generalized Gamma Mixture Model (GGMM) and Relevance Feedback Mechanism
K M Vara Prasad1, Ande Prasad2
1K M Vara Prasad Research Scholar, Department of Computer Science, Vikrama Simhapuri University, Nellore.
2Ande Prasad Professor, Department of Computer Science, Vikrama Simhapuri University, Nellore. 

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 507-5078 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6943018520/2020©BEIESP | DOI: 10.35940/ijrte.E6943.018520

Open Access | Ethics and Policies | Cite  | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The exponential growth of multimedia technologies facilitated the ease of developing various images having different shapes and scales. With the advent of mobile technologies, these messages so generated are being transmitted across the globe in different formats for different purposes. With these advancements methodologies thus developed for identifying or expressing subjects (individuals) their views by means of sketches. These sketch based images have many advantages, in particular, these images can be well considered in situations where the narration and capturing becomes difficult. The present article underlines a mechanism to interpret the images and also addresses the retrieval of such sketch based images using Generalized Gamma Mixture Model. The relevance feedback mechanism is utilized to retrieve more relevant to sketch based images based on the query image. The efficiency of the proposed word is evaluated using metrics like precision, recall, error rate, and retrieval accuracy.
Keywords: Sketch-Based Images, Performance Metrics, Evaluation, Precision, Recall, Accuracy.
Scope of the Article: Performance Evaluation of Networks.