Image classification using neural network for efficient image retrieval

Vegad, Sudhir; Italiya, Prashant K.

doi:10.1109/eesco.2015.7253860

Cited by 4 publications

(1 citation statement)

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“…Digital image processing field has been widely applied in various areas including the one of which is the application of content-based image retrieval (CBIR). The term CBIR was first appointed by Kato in 1992 [1].Imagery content in the form of color, shape, and texture of the object in the image is the main content of which is used to identify the image retrieval process [2]. Identify retrieval process refers to the similarity distance feature color, shape, and texture between the two images.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval

Yuhandri¹,

Madenda²,

Wibowo³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper proposes a method of extraction, classification and pattern recognition songket cloth texture. Features Chaincode pattern texture (Chain-code pattern texture features) are used as the basis songket search in the database or referred to as a texture-based songket pattern recognition which is part of a content-based image retrieval (CBIR). This method consists of two parts: the first is the process of establishing databases feature Chain-code pattern texture songket and training process of pattern recognition using backpropagation neural network (BPNN), the second is the retrieval process to recognize the pattern songket (songket pattern recognition and retrieval). The proposed method is a combination of several algorithms: color image segmentation, binarization, cropping, edge detection/pattern, feature extraction pattern (probability widened chain-code datasets) and BPNN training and test. Results of tests on 40 different songket motifs with training data showing the level of accuracy of the proposed method. Results of tests on 40 songket motifs show a good degree of accuracy of proposed method where the precision value reached 98% and recall value reached 99%.

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Section: Introductionmentioning

confidence: 99%