An Approach for Effective Image Retrievals Based on Semantic Tagging and Generalized Gaussian Mixture Model

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Anuradha. Padala 1,* Srinivas Yarramalle 2 MHM Krishna Prasad 3

1. Gitam University / Department of Computer Science & Engineering, Visakhapatnam, 530045, India

2. Gitam University / Department of Information Technology, Visakhapatnam, 530045, India

3. JNTUK / Department of Computer Science and Engineering, Kakinada, 533003, India

* Corresponding author.


Received: 6 Feb. 2015 / Revised: 11 Mar. 2015 / Accepted: 2 Apr. 2015 / Published: 8 May 2015

Index Terms

Social Networking, Flicker Database, Image Retrieval, Feature Extraction, Generalised GMM


The present day users navigate more using electronic gadgets, interacting with social networking sites and retrieving the images of interest from the information groups or similar groups. Most of the retrievals techniques are not much effective due to the semantic gap. Many models have been discussed for effective retrievals of the images based on feature extraction, label based and semantic rules. However effective retrievals of images are still a challenging task, model based techniques together with semantic attributes provide alternatives for efficient retrievals. This article is developed with the concepts of Generalized Gaussian Mixture Models and Semantic attributes. Flicker dataset is considered to experiment the model and efficiency is measured using Precision and Recall.

Cite This Paper

Anuradha. Padala, Srinivas. Yarramalle, Krishna Prasad. MHM, "An Approach for Effective Image Retrievals Based on Semantic Tagging and Generalized Gaussian Mixture Model", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.7, no.3, pp.39-44, 2015. DOI:10.5815/ijieeb.2015.03.06


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