Novel algorithm for exudate extraction from fundus images of the eye for a content based image retrieval system

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

doi:10.1109/iccsce.2014.7072763

Cited by 4 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They use to analyze the KTH dataset. Experimental results show 88.61Gururaj purposed a Novel approach for Finding Fundus Images of the Eyes through Extracted Exudate for a CBIR System [12]. They proposed a novel approach for the detection of the Optical disk and the presence of any exudates.…”

Section: Related Workmentioning

confidence: 98%

“…for each particle If the fitness value is better than the best fitness value (pBest) in history 12) set current value as the new pBest 13) End for 14) Choose the particle with the best fitness value of all the particles as the gBest 15) for each particle 16)…”

Section: Proposed Workmentioning

confidence: 99%

“…Select the best particle to be used by genetic operators 11) Generate new particles using crossover and mutation 12) Evaluate the fitness of the new particles 13)…”

Section: Proposed Workmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of Content Based Image Retrieval Using Hybrid Approach

Majeed

2022

QUESTRJ

View full text Add to dashboard Cite

With the dawn of multimedia technology and the social web, retrieval from large image databases become possible. As a consequence of rising usage and incredible enthusiasm for inquiry about on Content-Based Image Retrieval (CBIR) systems, it needs improvement in the accuracy of CBIR systems in addition to the decline in monotonous results. Currently, the majority of research has focused on the representation and differentiation of images by an arrangement of low-level visual features. However, most of the retrieval systems produce repetitive or unnecessary retrieval results, termed a redundancy factor. In addition, content-based retrieval with reduced redundancy has a direct correlation with high-level semantics which is overlooked. Accurate content retrieval with reduced redundancy enables the user to focus on the actual problem in preference to nurture the retrieval results, augments the efficiency, and improves overall system performance. To enhance the retrieval accuracy and diminish the redundancy factor in image retrieval, we proposed an optimization-based technique that is blended with classification. In the proposed novel hybrid approach for CBIR, we used a two-tier architecture model. The first tier belongs to the feature extraction process via Particle Swarm Optimization with a Support Vector Machine as a classifier. On the way to reduce the redundancy factor, the K-Nearest Neighbor as a classifier is used with the Genetic Algorithm in the second tier. We noted a noteworthy increase in retrieval accuracy for images that is up to 25% (approx.). The proposed hybrid model is effective to enhance accuracy and reduce redundancy factors in CBIR systems. We used the WANG dataset for experimentation. Henceforward, improving the retrieval accuracy and reducing redundancy factor using unsupervised learning techniques is part of our future work.

show abstract

Section: Related Workmentioning

confidence: 98%

Section: Proposed Workmentioning

confidence: 99%