Texture Fusion for Batik Motif Retrieval System

Abstract

<p>This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.</p>

Rancang Bangun Aplikasi Pengenalan Wajah menggunakan Metode Viola-Jones dan Algoritma PCA

“…Penelitian mengenai pengenalan wajah untuk mengidentifikasi seseorang berdasarkan pada citra inputan yang telah ada [16]. [9]. Adapun untuk penggunaan metode Viola-Jones dalam pendeteksian wajah disini pada blok citra masukkan yang masih mengandung komponen wajah dan bukan wajah dipisahkan [12], sehingga hanya ada komponen wajah yang dilewatkan pada blok ini.…”

Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition

“…From the experiments shows that PCA feature reduction can improve the retrieval precision while SFFS can reduce the execution time [13]. In 2017, Nurhaida et al developed Texture Fusion for Batik Motif Retrieval System, they systematically investigates the impact of image texture features on batik motif retrieval performance [14]. Based on the result of earlier work [7] and [13], this study was focused in batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM and using PCA feature reduction to improve the classification accuracy and reduce the computational time.…”

CBIR of Batik Images using Micro Structure Descriptor on Android

“…This knowledge needs to be introduced to the general public through an easily accessible application. Several studies on batik have been proposed, such as the Content Based Image Retrieval (CBIR) technique for retrieving batik images using Color Difference Histogram (CDH) [8], enhanced micro-structure descriptor [9], Multi Texton Co-Occurrence Descriptor [10], Multi Structure Co-occurrence Descriptor [11], improved completed robust local binary pattern [12], Texture Fusion [13]. However, all those studies are build for desktop.…”

“…Content Based Image Retrieval (CBIR) is one of active research nowdays, thus there are a lot of techniques proposed to seek the right method [14]- [17]. Techniques in CBIR, be it CDH and other techniques, such as texton co-occurrence matrix, multi-textons histogram, and micro-structure descriptor, depend on the feature extraction process [8]- [13]. Types of features can vary, such as color, texture, angle, and shape features.…”