Abstract
Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that SVM based RF approach generating better results for CBIR. Therefore, this paper focused on SVM based RF approach. To enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) before applying SVM on user feedback. The main objective of using these meta-heuristic was to increase the positive image sample size from SVM. Firstly steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO, finally SVM is applied using the positive sample generated through GA. The proposed technique is named as Particle Swarm Optimization Genetic Algorithm- Support Vector Machine Relevance Feedback (PSO-G A-SVM-RF). Precisions, recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 images. From experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.
Highlights
In this era of information technology due to various personal devices such as Digital camera, Smart phones, there is an increase in number of images, photos, videos and uploads on social media
The performance of Particle Swarm Optimization (PSO)-Genetic Algorithm (GA)-Support Vector Machine (SVM)-Relevance Feedback (RF) for the top 40 retrievals presented in Fig. 1 shows that the PSO-GA-SVM-RF approach succeeded to achieve 98% accuracy in final iteration, the performance of other techniques is not good
In case of top 80 retrievals the PSO-GA-SVM-RF approach achieved accuracy of 90% in 4th iteration but in final two iterations successfully achieved 96% accuracy while among other techniques only GOSVM and SEMIBDE achieved more than 50% in final iteration
Summary
In this era of information technology due to various personal devices such as Digital camera, Smart phones, there is an increase in number of images, photos, videos and uploads on social media. Due to these activities the volume of digital content are increasing day by day in various achieves. The most common methods for searching from multimedia contents is the use of information associated with the images such as keywords, labels, tags and timestamp where retrieval is performed through text based. The manual annotation provides the general information but is loosely connected to the specific visual content of the image which means a textual query can generate the multiple results with diverse semantic meaning
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