Implementasi Transformasi Curvelet Dan Ruang Warna HVS Untuk Temu Kembali Citra Batik Berbasis Isi Pada Situs Batik

Batik merupakan bagian dari kekayaan budaya bangsa Indonesia. Dengan banyaknya motif dari tiap daerah di Indonesia dan sebagai pelestarian warisan budaya Indonesia, diperlukan inventarisasi data dari tiap motif batik. Namun, mencari dan mendapatkan kembali citra batik yang diinginkan pada sekumpulan data motif batik yang besar tidaklah mudah. Temu kembali citra berbasis isi merupakan suatu metode untuk pengenalan citra batik melalui ekstraksi fitur isi citra untuk melihat, mencari, dan menemukan kembali citra dari koleksi besar. Untuk memecahkan permasalahan di atas, pada Tugas Akhir ini diimplementasikan sistem temu kembali citra yang efektif dan efisien menggunakan Transformasi Curvelet dan ruang warna HSV. Transformasi Curvelet adalah representasi multi-skala baru yang cocok untuk obyek dengan kurva. Ruang Warna HSV konsisten dengan persepsi manusia karena HSV merepresentasikan warna dalam cara yang mirip dengan bagaimana manusia berpikir. Dari tiap citra batik akan di ambil fitur tekstur dari energi dan s tandar deviasi dari koefisien curvelet dengan Transformasi Curvelet pada tiap wedge tiap skala. Untuk fitur warna diambil histogram ruang warna HSV yang telah terkuantisasi menjadi 72 bins warna. Dari fitur tersebut lalu dicari similaritas tiap citra batik dengan menggunakan jarak Canberra. Berdasarkan hasil uji coba yang dilakukan terhadap dataset Batik, metode ini menghasilkan rata-rata precision sebesar 96.85% menggunakan Transformasi Curvelet 4 skala dan Histogram kuantisasi ruang warna HSV. Hal ini mengindikasikan bahwa Transformasi Curvelet dan ruang warna HSV merupakan metode yang menjanjikan dalam proses temu kembali citra batik. ============================================================================================================================= Batik is part of the richness of Indonesian culture. With so many motifs of each region in Indonesia and the preservation of cultural heritage of Indonesia, inventory data from each motif is required. However, search for and retrieve the desired image of batik motif on a large data set is not easy. Content-based image retrieval is a method for batik image recognition through the contents of the image feature extraction to view, search, and rediscover the image of a great collection. To solve the above problems, this final project propose image retrieval system that implemented effectively and efficiently using Curvelet Transformation and HSV color space. Curvelet transformation is a new multi-scale representation suitable for objects with curves. HSV color space is consistent with human perception because HSV represents color in a way similar to how humans think. Of each image will be taken batik texture feature of the energy and standard deviation of curvelet coefficients with curvelet transform each wedge on each scale. Color feature take the color histogram feature HSV color space that has been quantized into 72 bins color. From the feature above, similarity of each batik image calculated using Canberra distance. Based on the results of experiments performed on a dataset of batik, this method resulted in an average precision of 96.85% using 4-scale Curvelet transform and quantization histogram of HSV color space. This indicates that the Curvelet Transform and HSV color space is a promising method in the process of batik image retrieval

Novel CBIR System Based on Ripplet Transform Using Interactive Neuro-Fuzzy Technique

Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management and retrieval paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to reduce the computational complexity, the proposed scheme utilizes a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed technique. The performance of the proposed CBIR system is evaluated using a 2 £ 5-fold cross validation followed by a statistical analysis. The experimental results suggest that the proposed system based on RT, performs better than many existing CBIR schemes based on other transforms, and the difference is statistically significant

Palm Print Recognition Using Curve let Transform

In the era of Information Technology, openness of the information is a major concern. As the confidentiality and integrity of the information is critically important, it has to be secured from unauthorized access. Traditional security and identification are not sufficient enough; people need to find a new authentic system based on behavioral & physiological characteristics of person which is called as Biometric. Palm print recognition gives several advantages over the other biometrics such as low resolution, low cost, non-intrusiveness and stable structure features. Now a days Palm print based personal verification system is used in many security application due to its ease of acquisition, high user acceptance and reliability. Various approaches which deal with palm recognition are texture approach, line approach and appearance approach. By using texture approach it is possible to obtain texture sample with low resolution and texture is much more stable as compare to line and appearance. This paper is aimed to analyze the performance of palm print recognition systems using Curvelet features and for dimension reduction PCA is used

Face detection in profile views using fast discrete curvelet transform (FDCT) and support vector machine (SVM)

Human face detection is an indispensable component in face processing applications, including automatic face recognition, security surveillance, facial expression recognition, and the like. This paper presents a profile face detection algorithm based on curvelet features, as curvelet transform offers good directional representation and can capture edge information in human face from different angles. First, a simple skin color segmentation scheme based on HSV (Hue - Saturation - Value) and YCgCr (luminance - green chrominance - red chrominance) color models is used to extract skin blocks. The segmentation scheme utilizes only the S and CgCr components, and is therefore luminance independent. Features extracted from three frequency bands from curvelet decomposition are used to detect face in each block. A support vector machine (SVM) classifier is trained for the classification task. In the performance test, the results showed that the proposed algorithm can detect profile faces in color images with good detection rate and low misdetection rate