Hybrid Information Retrieval Model For Web Images

The Bing Bang of the Internet in the early 90's increased dramatically the number of images being distributed and shared over the web. As a result, image information retrieval systems were developed to index and retrieve image files spread over the Internet. Most of these systems are keyword-based which search for images based on their textual metadata; and thus, they are imprecise as it is vague to describe an image with a human language. Besides, there exist the content-based image retrieval systems which search for images based on their visual information. However, content-based type systems are still immature and not that effective as they suffer from low retrieval recall/precision rate. This paper proposes a new hybrid image information retrieval model for indexing and retrieving web images published in HTML documents. The distinguishing mark of the proposed model is that it is based on both graphical content and textual metadata. The graphical content is denoted by color features and color histogram of the image; while textual metadata are denoted by the terms that surround the image in the HTML document, more particularly, the terms that appear in the tags p, h1, and h2, in addition to the terms that appear in the image's alt attribute, filename, and class-label. Moreover, this paper presents a new term weighting scheme called VTF-IDF short for Variable Term Frequency-Inverse Document Frequency which unlike traditional schemes, it exploits the HTML tag structure and assigns an extra bonus weight for terms that appear within certain particular HTML tags that are correlated to the semantics of the image. Experiments conducted to evaluate the proposed IR model showed a high retrieval precision rate that outpaced other current models.Comment: LACSC - Lebanese Association for Computational Sciences, http://www.lacsc.org/; International Journal of Computer Science & Emerging Technologies (IJCSET), Vol. 3, No. 1, February 201

COMPRESSED DOMAIN IMAGE INDEXING AND RETRIEVAL BASED ON THE MINIMAL SPANNING TREE

ABSTRACT In this paper, a method for content-based retrieval of JPEG images is presented, utilizing features directly from the discrete cosine transform (DCT) domain. Image indexing is achieved by extracting color and texture feature vectors, using an efficient technique applied on the DCT coefficients. Similarity between the query-and database-images is provided based on a statistical graph matching approach. The proposed measure makes use of the Wald-Wolfowitz test, a nonparametric test that assesses the commonality between two different sets of multivariate observations. Experimental results demonstrate the enhanced performance of our approach, compared to previously reported methods

Digital photo album management techniques: from one dimension to multi-dimension.

Lu Yang.Thesis submitted in: November 2004.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 96-103).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Our Contributions --- p.3Chapter 1.3 --- Thesis Outline --- p.5Chapter 2 --- Background Study --- p.7Chapter 2.1 --- MPEG-7 Introduction --- p.8Chapter 2.2 --- Image Analysis in CBIR Systems --- p.11Chapter 2.2.1 --- Color Information --- p.13Chapter 2.2.2 --- Color Layout --- p.19Chapter 2.2.3 --- Texture Information --- p.20Chapter 2.2.4 --- Shape Information --- p.24Chapter 2.2.5 --- CBIR Systems --- p.26Chapter 2.3 --- Image Processing in JPEG Frequency Domain --- p.30Chapter 2.4 --- Photo Album Clustering --- p.33Chapter 3 --- Feature Extraction and Similarity Analysis --- p.38Chapter 3.1 --- Feature Set in Frequency Domain --- p.38Chapter 3.1.1 --- JPEG Frequency Data --- p.39Chapter 3.1.2 --- Our Feature Set --- p.42Chapter 3.2 --- Digital Photo Similarity Analysis --- p.43Chapter 3.2.1 --- Energy Histogram --- p.43Chapter 3.2.2 --- Photo Distance --- p.45Chapter 4 --- 1-Dimensional Photo Album Management Techniques --- p.49Chapter 4.1 --- Photo Album Sorting --- p.50Chapter 4.2 --- Photo Album Clustering --- p.52Chapter 4.3 --- Photo Album Compression --- p.56Chapter 4.3.1 --- Variable IBP frames --- p.56Chapter 4.3.2 --- Adaptive Search Window --- p.57Chapter 4.3.3 --- Compression Flow --- p.59Chapter 4.4 --- Experiments and Performance Evaluations --- p.60Chapter 5 --- High Dimensional Photo Clustering --- p.67Chapter 5.1 --- Traditional Clustering Techniques --- p.67Chapter 5.1.1 --- Hierarchical Clustering --- p.68Chapter 5.1.2 --- Traditional K-means --- p.71Chapter 5.2 --- Multidimensional Scaling --- p.74Chapter 5.2.1 --- Introduction --- p.75Chapter 5.2.2 --- Classical Scaling --- p.77Chapter 5.3 --- Our Interactive MDS-based Clustering --- p.80Chapter 5.3.1 --- Principal Coordinates from MDS --- p.81Chapter 5.3.2 --- Clustering Scheme --- p.82Chapter 5.3.3 --- Layout Scheme --- p.84Chapter 5.4 --- Experiments and Results --- p.87Chapter 6 --- Conclusions --- p.94Bibliography --- p.9

From content-based to semantic image retrieval. Low level feature extraction, classification using image processing and neural networks, content based image retrieval, hybrid low level and high level based image retrieval in the compressed DCT domain.

Digital image archiving urgently requires advanced techniques for more efficient storage and retrieval methods because of the increasing amount of digital. Although JPEG supply systems to compress image data efficiently, the problems of how to organize the image database structure for efficient indexing and retrieval, how to index and retrieve image data from DCT compressed domain and how to interpret image data semantically are major obstacles for further development of digital image database system. In content-based image, image analysis is the primary step to extract useful information from image databases. The difficulty in content-based image retrieval is how to summarize the low-level features into high-level or semantic descriptors to facilitate the retrieval procedure. Such a shift toward a semantic visual data learning or detection of semantic objects generates an urgent need to link the low level features with semantic understanding of the observed visual information. To solve such a -semantic gap¿ problem, an efficient way is to develop a number of classifiers to identify the presence of semantic image components that can be connected to semantic descriptors. Among various semantic objects, the human face is a very important example, which is usually also the most significant element in many images and photos. The presence of faces can usually be correlated to specific scenes with semantic inference according to a given ontology. Therefore, face detection can be an efficient tool to annotate images for semantic descriptors. In this thesis, a paradigm to process, analyze and interpret digital images is proposed. In order to speed up access to desired images, after accessing image data, image features are presented for analysis. This analysis gives not only a structure for content-based image retrieval but also the basic units ii for high-level semantic image interpretation. Finally, images are interpreted and classified into some semantic categories by semantic object detection categorization algorithm

Exploiting image indexing techniques in DCT domain

This paper is concerned with the indexing and retrieval of images based on features extracted directly from the JPEG discrete cosine transform (DCT) domain. We examine possible ways of manipulating DCT coefficients by standard image analysis approaches to describe image shape, texture, and color. Through the Mandala transformation, our approach groups a subset of DCT coefficients to form ten blocks. Each block represents a particular frequency content of the original image. Two blocks are used to model rough object shape; nine blocks to describe subband properties; and one block to compute color distribution. As a result, the amount of data used for processing and analysis is significantly reduced. This can lead to simple yet efficient ways of indexing and retrieval in a large-scale image database. Experimental results show that our proposed approach offers superior indexing speed without significantly sacrificing the retrieval accuracy. © 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.link_to_subscribed_fulltex