CHAPTER 6 CONCLUSION AND FUTURE WORKS ...shodhganga.inflibnet.ac.in/bitstream/10603/33827/6/...131 CHAPTER 6 CONCLUSION AND FUTURE WORKS The problem of search and retrieval of images

131

CHAPTER 6

CONCLUSION AND FUTURE WORKS

The problem of search and retrieval of images in ever growing digital

technology has attracted tremendous attention in recent years from the research

community. Most of the CBIR systems are based on global features which use the

visual features. The main drawback of Global CBIR system is semantic gap. The

goal of this research is to address the problem of semantic gap using local features.

A methodology based on SUSAN and DCT local features a CBIR system was

implemented. The performance of the system was analyzed with and without colour

information of images. The Average Precision/Recall rate of the system without

colour information is 53.98%. The system proved to be better when the colour

characteristics of the images are combined with the local features and the Average

Precision/Recall rate was 80.56%. Further, the performance of system was also

analyzed by giving multiple queries. The Average Precision/Recall rate of colour

characteristics with multiple queries was 86.32%.

In the next approach, the Speeded Up Robust Features are used to retrieve

images. The system was implemented to retrieve images without adding any features.

The system was also implemented taking the colour feature of the images along with

the SURF features. This method also proves to be better in image retrieval when the

colour characteristics are taken. The Average Precision/Recall rate of the system

without colour information is 71.67%. The system proved to be better when the

colour characteristics of the images are combined with the local features and the

Average Precision/Recall rate for single query retrieval was 90.37% and for multiple

queries the Average Precision/Recall rate was 96.39%.

Final methodology was implemented using the Histogram of Oriented Gradients

descriptor and Harris corners detector. The Average Precision/Recall rate of the system

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132

without colour information is 84.07% and with colour characteristics for single query

was 96.76% and 96.33% for multiple queries.

In future this research can be enhanced which requires minimal user-

interaction, Further, the performance of the three methods could be analyzed with

large collection of images.

The time complexity for matching the key features is found to be high. Therefore

a faster method to reduce the time complexity of matching could be introduced. To

bridge the semantic gap, the Bag-of-Visual-Word and Model-based representations

could be used in the future work.


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[ZCY11] Zhengli Zhu, Chunxia Zhao, Yingkun Hou, Hua Gao, Rotation-Invariant Texture Image Retrieval Based on Combined Feature Sets,International Journal of Digital Content Technology and itsApplications. Volume 5, Number 3, March 2011.

[ZWCF00] Zhiyong Wang, Zheru Chi, Dagan Feng and Qing Wang , Leaf ImageRetrieval with Shape Features, Springer,VISUAL 2000, LNCS 1929,pp. 477−487, 2000.


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LIST OF PUBLICATIONS

[1] Lt. Dr. S.Santhosh Baboo, K.Velmurugan, “Content-Based Image Retrieval:

The Challenges and Techniques”, SAJOSPS, Vol.10,No.2,pp.120-122. June

2010.

[2] K.Velmurugan, Lt. Dr. S. Santhosh Baboo, “Content-Based Image Retrieval

using Interest points and kd-tree”,CTAC’11, Second National conference on

Current Trends in Advanced Computing, Kristu Jayanti College, Banglore, pp.

133-135,April 2011.

[3] K.Velmurugan, Lt. Dr. S. Santhosh Baboo, “Content-Based Image Retrieval

using SURF and Colour Moments”, GJCST , Volume 11 Issue 10 Version

1.0,pp. 1-4, May 2011.

[4] K.Velmurugan, Lt. Dr. S. Santhosh Baboo, “Image Retrieval using Harris

Corners and Hitogram of Oriented Gradients”, IJCA, Vol.24.No.7,pp. 6-10,

June 2011.


Documents

CHAPTER 6 CONCLUSION AND FUTURE WORKS ...shodhganga.inflibnet.ac.in/bitstream/10603/33827/6/...131 CHAPTER 6 CONCLUSION AND FUTURE WORKS The problem of search and retrieval of images