Texture Feature Extraction based on Local Weighting Pattern (LWP) using Fuzzy Logic Approach

Abstract

The texture feature description becomes a tremendous challenge in the field of computer vision and pattern recognition. The high-quality feature descriptor attributes some of which are unique, due to a large number of texture classes, robust against illumination variations, and low dimensional representations. A number of image feature extraction methods had been proposed, which can be divided into two categories: holistic and local image feature extraction. The holistic feature extraction method is very sensitive to changes in geometric shapes and some variations of illumination and noise. The local image feature extraction methods can effectively overcome those weaknesses. In this study, the texture features of an RGB image are built using the Local Weighting Pattern (LWP). By using the gray-level dynamic range modification technique, Fuzzy Membership Function (MF) is applied to LWP texture images to build Fuzzy-based LWP image (FLWP). From the resulting image is then used to generate a feature descriptor in the form of labels.

Implementasi Metode Local Weighting Pattern pada Prototype Ciri Frame Dinamis

“…Ekstraksi fitur pada penelitian ini menggunakan Local Weighting Pattern (LWP). LWP adalah pola yang dibangun berdasarkan bobot lokal piksel yang merupakan jumlah dari kode binernya yang dibangun berdasarkan semua nilai ambang batas antara piksel tetangga ke tengah piksel [6]. Hasil dari ekstraksi fitur setiap frame menggunakan LWP kemudian akan dihitung menggunakan metode frame difference untuk mengetahui perubahan yang terjadi.…”

“…Gaffar [6] mengungkapkan fitur tekstur menjadi sebuah tantangan dibidang computer vision dan pengenalan pola. Hal ini disebabkan fitur tekstur memiliki kualitas yang tinggi, salah satunya adalah tahan terhadap berbagai penerangan.…”

Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment

“…, ℎ(3)�Each linguistic value is represented using the Triangular Membership Function (MF) [19], as shown in Fig. 3.…”

Inter-Frame Video Compression based on Adaptive Fuzzy Inference System Compression of Multiple Frame Characteristics

“…Various studies in this field focus on how to build powerful feature extraction methods. Some of them are the Median Robust Extended Local Binary Pattern [26], Local Weighting Pattern [27], dan Gray-Level Dynamic Range Modification Technique [28]. Image features are also widely used in developing various feature-based image compressions algorithms [29][30][31] [32].…”