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

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

Neural network-based shape retrieval using moment invariants and Zernike moments.

Shape is one of the fundamental image features for use in Content-Based Image Retrieval (CBIR). Compared with other visual features such as color and texture, it is extremely powerful and provides capability for object recognition and similarity-based image retrieval. In this thesis, we propose a Neural Network-Based Shape Retrieval System using Moment Invariants and Zernike Moments. Moment Invariants and Zernike Moments are two region-based shape representation schemes and are derived from the shape in an image and serve as image features. k means clustering is used to group similar images in an image collection into k clusters whereas Neural Network is used to facilitate retrieval against a given query image. Neural Network is trained by the clustering result on all of the images in the collection using back-propagation algorithm. In this scheme, Neural Network serves as a classifier such that moments are inputs to the Neural Network and the output is one of the k classes that have the largest similarities to the query image. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C444. Source: Masters Abstracts International, Volume: 44-03, page: 1396. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Color Image Clustering using Block Truncation Algorithm

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters

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

Content-based indexing of low resolution documents

In any multimedia presentation, the trend for attendees taking pictures of slides that interest them during the presentation using capturing devices is gaining popularity. To enhance the image usefulness, the images captured could be linked to image or video database. The database can be used for the purpose of file archiving, teaching and learning, research and knowledge management, which concern image search. However, the above-mentioned devices include cameras or mobiles phones have low resolution resulted from poor lighting and noise. Content-Based Image Retrieval (CBIR) is considered among the most interesting and promising fields as far as image search is concerned. Image search is related with finding images that are similar for the known query image found in a given image database. This thesis concerns with the methods used for the purpose of identifying documents that are captured using image capturing devices. In addition, the thesis also concerns with a technique that can be used to retrieve images from an indexed image database. Both concerns above apply digital image processing technique. To build an indexed structure for fast and high quality content-based retrieval of an image, some existing representative signatures and the key indexes used have been revised. The retrieval performance is very much relying on how the indexing is done. The retrieval approaches that are currently in existence including making use of shape, colour and texture features. Putting into consideration these features relative to individual databases, the majority of retrievals approaches have poor results on low resolution documents, consuming a lot of time and in the some cases, for the given query image, irrelevant images are obtained. The proposed identification and indexing method in the thesis uses a Visual Signature (VS). VS consists of the captures slides textual layout’s graphical information, shape’s moment and spatial distribution of colour. This approach, which is signature-based are considered for fast and efficient matching to fulfil the needs of real-time applications. The approach also has the capability to overcome the problem low resolution document such as noisy image, the environment’s varying lighting conditions and complex backgrounds. We present hierarchy indexing techniques, whose foundation are tree and clustering. K-means clustering are used for visual features like colour since their spatial distribution give a good image’s global information. Tree indexing for extracted layout and shape features are structured hierarchically and Euclidean distance is used to get similarity image for CBIR. The assessment of the proposed indexing scheme is conducted based on recall and precision, a standard CBIR retrieval performance evaluation. We develop CBIR system and conduct various retrieval experiments with the fundamental aim of comparing the accuracy during image retrieval. A new algorithm that can be used with integrated visual signatures, especially in late fusion query was introduced. The algorithm has the capability of reducing any shortcoming associated with normalisation in initial fusion technique. Slides from conferences, lectures and meetings presentation are used for comparing the proposed technique’s performances with that of the existing approaches with the help of real data. This finding of the thesis presents exciting possibilities as the CBIR systems is able to produce high quality result even for a query, which uses low resolution documents. In the future, the utilization of multimodal signatures, relevance feedback and artificial intelligence technique are recommended to be used in CBIR system to further enhance the performance

Multi Features Content-Based Image Retrieval Using Clustering And Decision Tree Algorithm

The classification can be performed by using the decision tree approach. Previous researches on the classification using the decision tree have mostly been intended to classify text data. This paper was intended to introduce a classification application to the content-based image retrieval (CBIR) with multi-attributes by using a decision tree. The attributes used were the visual features of the image, i.e. : color moments (order 1, 2 and 3), image entropy, energy and homogeneity. K-means cluster algorithm was used to categorize each attribute. The result of categorized data was then built into a decision tree by using C4.5. To show the concept in application, this research built an application with main features, i.e.: cases data input, cases list, training process and testing process to do classification. The resulting tests of 150 rontgen data showed the training data classification’s truth value of 75.33% and testing data classification of 55.7%

The Optimisation of Elementary and Integrative Content-Based Image Retrieval Techniques

Image retrieval plays a major role in many image processing applications. However, a number of factors (e.g. rotation, non-uniform illumination, noise and lack of spatial information) can disrupt the outputs of image retrieval systems such that they cannot produce the desired results. In recent years, many researchers have introduced different approaches to overcome this problem. Colour-based CBIR (content-based image retrieval) and shape-based CBIR were the most commonly used techniques for obtaining image signatures. Although the colour histogram and shape descriptor have produced satisfactory results for certain applications, they still suffer many theoretical and practical problems. A prominent one among them is the well-known “curse of dimensionality “. In this research, a new Fuzzy Fusion-based Colour and Shape Signature (FFCSS) approach for integrating colour-only and shape-only features has been investigated to produce an effective image feature vector for database retrieval. The proposed technique is based on an optimised fuzzy colour scheme and robust shape descriptors. Experimental tests were carried out to check the behaviour of the FFCSS-based system, including sensitivity and robustness of the proposed signature of the sampled images, especially under varied conditions of, rotation, scaling, noise and light intensity. To further improve retrieval efficiency of the devised signature model, the target image repositories were clustered into several groups using the k-means clustering algorithm at system runtime, where the search begins at the centres of each cluster. The FFCSS-based approach has proven superior to other benchmarked classic CBIR methods, hence this research makes a substantial contribution towards corresponding theoretical and practical fronts

Multi evidence fusion scheme for content-based image retrieval by clustering localised colour and texture features

Content-Based Image Retrieval (CBIR) is an automatic process of retrieving images according to their visual content. Research in this field mainly follows two directions. The first is concerned with the effectiveness in describing the visual content of images (i.e. features) by a technique that lead to discern similar and dissimilar images, and ultimately the retrieval of the most relevant images to the query image. The second direction focuses on retrieval efficiency by deploying efficient structures in organising images by their features in the database to narrow down the search space. The emphasis of this research is mainly on the effectiveness rather than the efficiency. There are two types of visual content features. The global feature represents the entire image by a single vector, and hence retrieval by using the global feature is more efficient but often less accurate. On the other hand, the local feature represents the image by a set of vectors, capturing localised visual variations in different parts of an image, promising better results particularly for images with complicated scenes. The first main purpose of this thesis is to study different types of local features. We explore a range of different types of local features from both frequency and spatial domains. Because of the large number of local features generated from an image, clustering methods are used for quantizing and summarising the feature vectors into segments from which a representation of the visual content of the entire image is derived. Since each clustering method has a different way of working and requires settings of different input parameters (e.g. number of clusters), preparations of input data (i.e. normalized or not) and choice of similarity measures, varied performance outcomes by different clustering methods in segmenting the local features are anticipated. We therefore also intend to study and analyse one commonly used clustering algorithm from each of the four main categories of clustering methods, i.e. K-means (partition-based), EM/GMM (model-based), Normalized Laplacian Spectral (graph-based), and Mean Shift (density-based). These algorithms were investigated in two scenarios when the number of clusters is either fixed or adaptively determined. Performances of the clustering algorithms in terms of image classification and retrieval are evaluated using three publically available image databases. The evaluations have revealed that a local DCT colour-texture feature was overall the best due to its robust integration of colour and texture information. In addition, our investigation into the behaviour of different clustering algorithms has shown that each algorithm had its own strengths and limitations in segmenting local features that affect the performance of image retrieval due to variations in visual colour and texture of the images. There is no algorithm that can outperform the others using either an adaptively determined or big fixed number of clusters. The second focus of this research is to investigate how to combine the positive effects of various local features obtained from different clustering algorithms in a fusion scheme aiming to bring about improved retrieval results over those by using a single clustering algorithm. The proposed fusion scheme integrates effectively the information from different sources, increasing the overall accuracy of retrieval. The proposed multi-evidence fusion scheme regards scores of image retrieval that are obtained from normalizing distances of applying different clustering algorithms to different types of local features as evidence and was presented in three forms: 1) evidence fusion using fixed weights (MEFS) where the weights were determined empirically and fixed a prior; 2) evidence fusion based on adaptive weights (AMEFS) where the fusion weights were adaptively determined using linear regression; 3) evidence fusion using a linear combination (Comb SUM) without weighting the evidences. Overall, all three versions of the multi-evidence fusion scheme have proved the ability to enhance the accuracy of image retrieval by increasing the number of relevant images in the ranked list. However, the improvement varied across different feature-clustering combinations (i.e. image representation) and the image databases used for the evaluation. This thesis presents an automatic method of image retrieval that can deal with natural world scenes by applying different clustering algorithms to different local features. The method achieves good accuracies of 85% at Top 5 and 80% at Top 10 over the WANG database, which are better when compared to a number of other well-known solutions in the literature. At the same time, the knowledge gained from this research, such as the effects of different types of local features and clustering methods on the retrieval results, enriches the understanding of the field and can be beneficial for the CBIR community