Implementasi dan Analisis Content-Based Image Retrieval pada Citra X-Ray menggunakan Algoritma Hierarki dan Algoritma Fast Genetic K-Means

ABSTRAKSI: Image Retrieval adalah proses melihat, mencari, dan mengambil citra dari basis data citra yang besar. Salah satu jenis Image Retrieval yang sangat terkenal adalah Content-Based Image Retrieval, yaitu proses pengambilan citra yang menggunakan ciri-ciri visual dari citra. Salah satu proses yang paling penting dalam sistem Content-Based Image Retrieval adalah preprocessing berupa klasterisasi citra. Proses ini dilakukan untuk mempercepat pengambilan citra dan meningkatkan akurasi dalam pencarian citra. Tugas akhir ini menggunakan Algoritma Hirarki dan Algoritma Fast Genetic K-Means dalam melakukan klasterisasi citra. Proses yang dilakukan adalah dengan mengekstrak ciri citra xray yang telah di-resize dengan menggunakan transformasi Haar Wavelet lalu diklaster berdasarkan bagian tubuhnya. Pengujian dilakukan dengan beberapa skenario untuk dilihat sistem dilihat dari pengaruh operator Algoritma Fast Genetic K-Means dalam terhadap nilai TWCV dan akurasi serta hasil evaluasi sistem Content-Based Image Retrieval dengan parameter precision dan recall. Hasil yang didapatkan dari pengujian yang dilakukan adalah klasterisasi citra dapat diimplementasikan dengan menggunakan algoritma Hirarki dan Algoritma Fast Genetic K-Means dengan akurasi yang didapatkan adalah 83,75%, nilai precision 0,72925, dan nilai recall 0,711.Kata Kunci : citra, klasterisasi, Fast Genetic K-Means, image retrieval, hirarkiABSTRACT: Image retrieval is the process of browsing searching, and retrieving images from a large database of digital image. One of image retrieval system present today is content-based image retrieval, which is the image retrieving process using visual features. One of useful process in Content-Based Image Retrieval system is preprocessing in image clustering. This process has been treated for speeding up image retrieval in image database and improving accuracy. This final project uses Hierarchical Algorithm and Fast Genetic KMeans Algorithm in image clustering. The process is done with extracting the xray features which is have resized using Haar Wavelet, then clusterizing based on parts of body. Tests carried out with several scenarios to see the system from he influence of Fast Genetic K-Means operators to TWCV value and Content-Based Image Retrieval system evaluation values using precision and recall. The results of testing system, image clustering can be implemented using Hierarchical algorithm and Fast Genetic K-Means algorithm with 83,75% accuracy, precision 0,72925, and recall 0,711.Keyword: image, clustering, Fast Genetic K-Means, image retrieval

Feature Selection for Image Retrieval based on Genetic Algorithm

This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm

Content Based Image Retrieval by Preprocessing Image Database

Increase in communication bandwidth, information content and the size of the multimedia databases have given rise to the concept of Content Based Image Retrieval (CBIR). Content based image retrieval is a technique that enables a user to extract similar images based on a query, from a database containing a large amount of images. A basic issue in designing a content based image retrieval system is to select the image features that best represent image content in a database. Current research in this area focuses on improving image retrieval accuracy. In this work, we have presented an ecient system for content based image retrieval. The system exploits the multiple features such as color, edge density, boolean edge density and histogram information features. The existing methods are concentrating on the relevance feedback techniques to improve the count of similar images related to a query from the raw image database. In this thesis, we propose a dierent strategy called preprocessing image database using k means clustering and genetic algorithm so that it will further helps to improve image retrieval accuracy. This can be achieved by taking multiple feature set, clustering algorithm and tness function for the genetic algorithms. Preprocessing image database is to cluster the similar images as homogeneous as possible and separate the dissimilar images as heterogeneous as possible. The main aim of this work is to nd the images that are most similar to the query image and new method is proposed for preprocessing image database via genetic algorithm for improved content based image retrieval system. The accuracy of our approach is presented by using performance metrics called confusion matrix, precison graph and F-measures. The clustering purity in more than half of the clusters has been above 90 percent purity

Pengelompokan Gambar Berdasarkan Fitur Warna Dan Tekstur Menggunakan FGKA Clustering (Fast Genetics K-Means Algorithm) Untuk Pencocokan Gambar

A large collections of digital images are being created. Usually, the only way of searching these collections was by using meta data (like caption or keywords). This way is not effective, impractical, need a big size of database and giving inaccurate result. Recently, it has been developed many ways in image retrieval that use image content (color, shape, and texture) that more recognised with CBIR ( Content Based Images Retrieval). The use of centroid produced from clustered HSV Histogram and Gabor Filter using FGKA, can be used for searching parameter. FGKA is merger of Genetic Algorithm and Kmeans Clustering Algorithm. FGKA is always converge to global optimum. Image Clustering and Matching based on color-texture feature are better than based on color feature only, texture only or using non-clustering method. Keywords: Genetics Algorithm, K-Means Clustering, CBIR, HSV Histogram, Gabor Filter

Hybrid Genetic Algorithm for Medical Image Feature Extraction and Selection

AbstractFor a hybrid medical image retrieval system, a genetic algorithm (GA) approach is presented for the selection of dimensionality reduced set of features. This system was developed in three phases. In first phase, three distinct algorithm are used to extract the vital features from the images. The algorithm devised for the extraction of the features are Texton based contour gradient extraction algorithm, Intrinsic pattern extraction algorithm and modified shift invariant feature transformation algorithm. In the second phase to identify the potential feature vector GA based feature selection is done, using a hybrid approach of “Branch and Bound Algorithm” and “Artificial Bee Colony Algorithm” using the breast cancer, Brain tumour and thyroid images. The Chi Square distance measurement is used to assess the similarity between query images and database images. A fitness function with respect Minimum description length principle were used as initial requirement for genetic algorithm. In the third phase to improve the performance of the hybrid content based medical image retrieval system diverse density based relevance feedback method is used. The term hybrid is used as this system can be used to retrieve any kind of medical image such as breast cancer, brain tumour, lung cancer, thyroid cancer and so on. This machine learning based feature selection method is used to reduce the existing system dimensionality problem. The experimental result shows that the GA driven image retrieval system selects optimal subset of feature to identify the right set of images

PENGELOMPOKAN GAMBAR BERDASARKAN WARNA DAN BENTUK MENGGUNAKAN FGKA (FAST GENETIC KMEANS ALGORITHM) UNTUK PENCOCOKAN GAMBAR

A large Collection of digital images in many areas of aspect are being created. The collection images are digitizing result of analogue photographs, diagrams, paintings, drawings, prints. Usually,the way of searching these collections was by indexing and image information based on text (like caption or keywords). This way is not effective and efficient because two reasons, are big size of database and subjective in picture meaning. Recently, it has been developed many ways in image retrieval that use image content (color, shape, and texture). The use of centroid produced from clustered RGB Histogram and Edge Detection matrix using FGKA, can be used for searching parameter. FGKA is merger of Genetic Algorithm and Kmeans Clustering Algorithm. FGKA is also developed from Genetic Kmeans Algorithm (GKA) which is always converge to global optimum. Image Clustering and Matching based on color-shape feature are better than based on color feature only if using some data wich are greatly into shape feature

Dynamic Learning of Indexing Concepts for Home Image Retrieval

International audienceThis paper presents a component of a content based image retrieval system dedicated to let a user define the indexing terms used later during retrieval. A user inputs a indexing term name, image examples and counter-examples of the term,and the system learns a model of the concept as well as a similarity measure for this term. The similarity measure is based on weights reflecting the importance of each low-level feature extracted from the images. The system computes these weights using a genetic algorithm. Rating a particular similarity measure is done by clustering the examples and counter-examples using these weights and computing the quality of the obtained clusters. Experiments are conducted and results are presented on a set of 600 images

An Integration of Genetic Algorithm and Projected Clustering for Optimization of Content Based Image Retrieval System

In recent years especially in the last decade, the rapid development in computers, storage media and digital image capturing devices enable to collect a large number of digital information and store the minicomputer readable formats. The main objective of this paper is to build more generalized CBIR system which increase the searching ability and provide more accurate results. To improve the retrieval accuracy the system has taken the feedback from the user automatically. To evaluate the performance of new system, we use WANG database. The metrics used for evaluation are precision, recall and retrieval time. The performance can be evaluated by comparing some existing systems in CBIR. The performance of new system in terms of the metrics proves to good

Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects

Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works