IMPLEMENTASI DAN ANALISIS SALIENT POINT DETECTOR BERDASARKAN WAVELET PADA CBIR (CONTENT BASED IMAGE RETRIEVAL)

ABSTRAKSI: Citra digital saat ini memiliki peran yang penting pada banyak bidang, seperti pada bidang pendidikan, seni, pemerintahan, perdagangan, kesehatan, dan pencegahan kriminal. Pencarian citra menjadi salah satu hal penting yang dibutuhkan mengingat semakin besarnya koleksi data citra. Saat ini berkembang teknologi pencarian citra berdasarkan isi visual dari citra yang disebut Content Based Image Retrieval (CBIR). Dalam Tugas Akhir ini akan dibangun sistem CBIR dengan metode Wavelet Based Salient Point Detector.Fitur dari citra diekstrak menggunakan Discrete Wavelet Transform yang kemudian ditentukan salient point dari subband wavelet yang didapatkan. Wavelet yang digunakan yaitu Haar wavelet. Penghitungan similarity menggunakan Canberra distance. Penghitungan performansi dari sistem ini dilakukan dengan menggunakan Mean Average Precision.Pengujian dilakukan untuk mengetahui parameter-parameter terbaik yang dapat meningkatkan performansi sistem CBIR ini. Dari hasil pengujian, didapatkan performansi terbaik Mean Average Precision sebesar 0.387 dengan parameter level dekomposisi 3 dan komposisi subband wavelet yang dominan pada CD(HH).Kata Kunci : Wavelet Based Salient Point Detector, Discrete Wavelete Transform , Haar wavelet, Canberra distance, Mean Average PrecisionABSTRACT: Today’s digital imagery has an important role in many fields, such as on education, health, art, government, commerce, health, and crime prevention. Search image become on of the important things that we needed, its because of the increasing amount of image data collection. Currently, there is a search technology that based on the image of the visual content of images called Content Based Image Retrieval (CBIR).In this Final Paper, CBIR system will be constructed by the method of Wavelet-Based Salient Point Detector. Feature of the image is extracted using Discrete Wavelet Transform Salient Point are then determined from the obtained wavelet subband. Wavelet that used in this system is Haar wavelet. Similarity calculation using Canberra distance. Calculation of performance’s system using Mean Average Precision.System testing is performed to determine the best parameters that can improve the performance of this CBIR system. From the test result, the best performance with Mean Average Precision is 0.387 with parameter level of decomposition in 3 level and the composition of the wavelet subband is dominant in the CD (HH).Keyword: Wavelet-based Salient Point Detector, Wavelete Discrete Transform, Haar wavelet, Canberra distance, Mean Average Precisio

A Fully Unsupervised Texture Segmentation Algorithm

This paper presents a fully unsupervised texture segmentation algorithm by using a modified discrete wavelet frames decomposition and a mean shift algorithm. By fully unsupervised, we mean the algorithm does not require any knowledge of the type of texture present nor the number of textures in the image to be segmented. The basic idea of the proposed method is to use the modified discrete wavelet frames to extract useful information from the image. Then, starting from the lowest level, the mean shift algorithm is used together with the fuzzy c-means clustering to divide the data into an appropriate number of clusters. The data clustering process is then refined at every level by taking into account the data at that particular level. The final crispy segmentation is obtained at the root level. This approach is applied to segment a variety of composite texture images into homogeneous texture areas and very good segmentation results are reported