Image Annotation and Retrieval with Generalized Gaussian Mixture Model Algorithm and Split Merge Expectation Maximalization Algorithm

ABSTRAKSI: Riset yang berhubungan tentang Image Annotation and Retrieval telah sangat berkembang saat ini. Dimulai dengan mimpi tentang bagaimana cara untuk mengorganisasikan sekumpulan citra skala besar tanpa melihat dulu isi citra tersebut, dan pada tahun 90 an muncul ide untuk mengorganisasikan citra dengan melihat isi dari citra tersebut atau lebih sering disebut dengan Content-Based Image Retrieval (CBIR). Salah satu metode baru yang dapat digunakan untuk meretrieve citra adalah Supervised Learning of Semantic Classes. Dalam membentuk model matematis, Supervised Learning standar menggunakan Gaussian Mixture Model dan Expectation Maximaliztion untuk Maximum Likelihood Estimationnya. Dalam tugas akhir ini, penulis berusaha mengganti model matematis pada Supervised Learning tersebut menggunakan Generalized Gaussian Mixture Model untuk mixture model-nya dan Split Merge Expectation Maximaliztion untuk Maximum Likelihood Estimation-nya. Berdasarkan hasil uji, secara umum metode Supervised Learning dengan GGMM-SMEM menghasilkan citra retrieve yang lebih akurat dibanding dengan menggunakan GMM-EMKata Kunci : content based image retrieval, image annotation, image retrieval, supervised learning of semantic classes, gaussian mixture model, generalized gaussian mixture model, expectation maximalization, split merge expectation maximalization, maximum likelihood eABSTRACT: Research about Image Annotation and Retrieval now is more developed. Starting from dream about how to organize a group of large scale images without knowing it\u27s content and at early 90\u27s there is an idea to organize images with knowing it\u27s content, it\u27s call Content-Based Image Retrieval (CBIR). One of new method that can use to retrieve image is Supervised Learning of Sematic Classes. In making mathematic models, Supervised Learning standard using Gaussian Mixture Model for mixture model and Expectation Maximaliztion for Maximum Likelihood Estimation. In this paper, author want to change how to make the mathematic models in Supervised Learning with Generalized Gaussian Mixture Model and Split Merge Expectation Maximaliztion for Maximum Likelihood Estimation. Based on result, generaly SML method with GGMM-SMEM retrieve images more accurate than SML method with GMM-EMKeyword: content based image retrieval, image annotation, image retrieval, supervised learning of semantic classes, gaussian mixture model, generalized gaussian mixture model, expectation maximalization, split merge expectation maximalization, maximum likelihood

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements

Relating visual and semantic image descriptors

This paper addresses the automatic analysis of visual content and extraction of metadata beyond pure visual descriptors. Two approaches are described: Automatic Image Annotation (AIA) and Confidence Clustering (CC). AIA attempts to automatically classify images based on two binary classifiers and is designed for the consumer electronics domain. Contrastingly, the CC approach does not attempt to assign a unique label to images but rather to organise the database based on concepts