Content Based Image Retrieval by using Multi Layer Centroid Contour Distance

Arai, Kohei; Rahmad, Cahya

doi:10.14569/ijarai.2013.020303

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…shows that the TECNO-CBIR approach decreased by 5-10Arai takes the dataset of Simulation and plankton and introduced a new approach to feature Shape by computing the distance between the centroid and object boundary. They utilize Multi-layer Centroid Contour Distance (MLCCD) [7]. Papers use the Shape feature.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Content Based Image Retrieval Using Hybrid Approach

Majeed

2022

QUESTRJ

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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.

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Section: Related Workmentioning

confidence: 99%

Optimization of Content Based Image Retrieval Using Hybrid Approach

Majeed

2022

QUESTRJ

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“…The proposed method can also be applied in the case of condensed clustering. we implemented multi-layer centroid contour distance (mlccd) [5] with some modification to the clustering.…”

Section: Proposed Clustering Algorithmmentioning

confidence: 99%

Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other)

Arai¹,

Rahmad²

2013

IJARAI

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Abstract-Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in the data sets. In this paper, we propose to provide a consistent partitioning of a dataset which allows identifying any shape of cluster patterns in case of numerical clustering, convex or non-convex. The method is based on layered structure representation that be obtained from measurement distance and angle of numerical data to the centroid data and based on the iterative clustering construction utilizing a nearest neighbor distance between clusters to merge. Encourage result show the effectiveness of the proposed technique.

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Three Precise Spatial Signatures for Image Copy Recall

alZahir

Bayaa

2018

2018 IEEE Canadian Conference on Electrical &Amp; Computer Engineering (CCECE)

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Content Based Image Retrieval by using Multi Layer Centroid Contour Distance

Cited by 5 publications

References 9 publications

Optimization of Content Based Image Retrieval Using Hybrid Approach

Optimization of Content Based Image Retrieval Using Hybrid Approach

Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other)

Three Precise Spatial Signatures for Image Copy Recall

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