Enhanced image annotations based on spatial information extraction and ontologies

Current research on image annotation often represents images in terms of labelled regions or objects, but pays little attention to the spatial positions or relationships between those regions or objects. To be effective, general purpose image retrieval systems require images with comprehensive annotations describing fully the content of the image. Much research is being done on automatic image annotation schemes but few authors address the issue of spatial annotations directly. This paper begins with a brief analysis of real picture queries to librarians showing how spatial terms are used to formulate queries. The paper is then concerned with the development of an enhanced automatic image annotation system, which extracts spatial information about objects in the image. The approach uses region boundaries and region labels to generate annotations describing absolute object positions and also relative positions between pairs of objects. A domain ontology and spatial information ontology are also used to extract more complex information about the relative closeness of objects to the viewer

Color image segmentation using multispectral random field texture model & color content features

This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The image is segmented into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the combination of MSAR and color features. The performance of the system is tested on two databases containing synthetic mosaics of natural textures and natural scenes, respectivelyFacultad de Informátic

Color image segmentation using multispectral random field texture model & color content features

This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The image is segmented into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the combination of MSAR and color features. The performance of the system is tested on two databases containing synthetic mosaics of natural textures and natural scenes, respectivelyFacultad de Informátic

CAMEL: Concept Annotated iMagE Libraries

Copyright 2001 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.410975The problem of content-based image searching has received considerable attention in the last few years. Thousands of images are now available on the internet, andmany important applications require searching of images in domains such as E-commerce, medical imaging, weather prediction, satellite imagery, and so on. Yet, content-based image querying is still largely unestablished as a mainstream field, nor is it widely used by search engines. We believe that two of the major hurdles for this poor acceptance are poor retrieval quality and usability. In this paper, we introduce the CAMEL system—an acronym for Concept Annotated iMagE Libraries—as an effort to address both of the above problems. The CAMEL system provides and easy-to-use, and yet powerful, text-only query interface, which allows users to search for images based on visual concepts, identified by specifying relevant keywords. Conceptually, CAMEL annotates images with the visual concepts that are relevant to them. In practice, CAMEL defines visual concepts by looking at sample images off-line and extracting their relevant visual features. Once defined, such visual concepts can be used to search for relevant images on the fly, using content-based search methods. The visual concepts are stored in a Concept Library and are represented by an associated set of wavelet features, which in our implementation were extracted by the WALRUS image querying system. Even though the CAMEL framework applies independently of the underlying query engine, for our prototype we have chosenWALRUS as a back-end, due to its ability to extract and query with image region features. CAMEL improves retrieval quality because it allows experts to build very accurate representations of visual concepts that can be used even by novice users. At the same time, CAMEL improves usability by supporting the familiar text-only interface currently used by most search engines on the web. Both improvements represent a departure from traditional approaches to improving image query systems—instead of focusing on query execution, we emphasize query specification by allowing simpler and yet more precise query specification

Constrained Querying of Multimedia Databases

Copyright 2001 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.410976This paper investigates the problem of high-level querying of multimedia data by imposing arbitrary domain-specific constraints among multimedia objects. We argue that the current structured query mode, and the query-by-content model, are insufficient for many important applications, and we propose an alternative query framework that unifies and extends the previous two models. The proposed framework is based on the querying-by-concept paradigm, where the query is expressed simply in terms of concepts, regardless of the complexity of the underlying multimedia search engines. The query-by-concept paradigm was previously illustrated by the CAMEL system. The present paper builds upon and extends that work by adding arbitrary constraints and multiple levels of hierarchy in the concept representation model. We consider queries simply as descriptions of virtual data set, and that allows us to use the same unifying concept representation for query specification, as well as for data annotation purposes. We also identify some key issues and challenges presented by the new framework, and we outline possible approaches for overcoming them. In particular, we study the problems of concept representation, extraction, refinement, storage, and matching

RISE: A ROBUST IMAGE SEARCH ENGINE

This thesis advances RISE (Robust Image Search Engine), an image database application designed to build and search an image repository. rise is built on the foundation of a CBIR (Content Based Image Retrieval) system. The basic goal of this system is to compute content similarity of images based on their color signatures. The color signature of an image is computed by systematically dividing the image into a number of small blocks and computing the average color of each block using ideas from DCT (Discrete Cosine Transform) that forms the basis for JPEG (Joint Photographic Experts Group) compression format. The average color extracted from each block is used to construct a tree structure and finally, the tree structure is compared with similar structures already stored in the database. During the query process, an image is given to the system as a query image and the system returns a set of images that have similar content or color distribution as the given image. The query image is processed to create its signature which is then matched against similar signature of images already stored in the database. The content similarity is measured by computing normalized Euclidean distance between the query image and the images already stored in the database. RISE has a GUI (Graphic User Interface) front end and a Java servlet in the back end that searches the images stored in the database and returns the results to the web browser. RISE enhances the performance of image operations of the system using JAI (Java Advance Imaging) tools

RISE: A Robust Image Search Engine

In this article we address the problem of organizing images for effective and efficient retrieval in large image database systems. Specifically, we describe the design and architecture of RISE, a Robust Image Search Engine. RISE is designed to build and search an image repository, with an interface that allows for the query and maintenance of the database over the Internet using any browser. RISE is built on the foundation of a CBIR (Content Based Image Retrieval) system and computes the similarity of images using their color signatures. The signature of an image in the database is computed by systematically dividing the image into a set of small blocks of pixels and then computing the average color of each block. This is based on the Discrete Cosine Transform (DCT) that forms the basis for popular JPEG image file format. The average color in each pixel block forms the characters of our image description. Organizing these pixel blocks into a tree structure allows us to create the words or tokens for the image. Thus the tokens represent the spatial distribution of the color in the image. The tokens for each image in the database are first computed and stored in a relational database as their signatures. Using a commercial relational database system (RDBMS) to store and query signatures of images improves the efficiency of the system. A query image provided by a user is first parsed to build the tokens which are then compared with the tokens for images in the database. During the query process, tokenization improves the efficiency by quantifying the degree of match between the query image and images in the database. The content similarity is measured by computing normalized Euclidean distance between corresponding tokens in query and stored images where correspondence is defined by the relative location of those tokens. The location of pixel blocks is maintained by using a quad tree structure that also improves performance by early pruning of search space. The distance is computed in perceptual color space, specifically L * a * b * and at different levels of detail. The perceptual color space allows RISE to ignore small variations in color while different levels of detail allow it to select a set of images for further exploration, or discard a set altogether. RISE only compares the precomputed color signature images that are stored in an RDBMS. It is very efficient since there is no need to extract complete information for every image. RISE is implemented using object-oriented design techniques and is deployed as a web browser-based search engine. RISE has a GUI (Graphical User Interface) front-end and a Java servlet in the back-end that searches the images stored in the database and returns the results to the web browser. RISE enhances the performance of image operations of the system by using JAI (Java Advance Imaging) tools, which obviates the dependence on a single image file format. In addition, the use of RDBMS and Java also facilitates the portability of 1 2 Goswami, Bhatia, Samal the system

Image Retreival Using Weighted Color Co-occurrence Histogram

Color image retrieval is to search color images using queries represented by image descriptors, which usually describe color distribution and relation of color pixels in an image. A color co-occurrence histogram (CCH) among the descriptors captures information on the spatial layout of colors within an image. It has shown excellent performance on color image retrieval, but requires many bins to describe contents of images and has bad effect on the similarity of same contents images, in which the size of homogeneous color regions are highly different. To resolve these problems and to improve retrieval performance, this thesis proposes a weighted CCH and two image retrieval methods using it. Generally the process of image retrieval using a CCH has three steps. The first step is to get the CCH from a query image. The second step is to compute similarity between CCHs of the query image and reference images. The last step is to sort reference images by the similarities and to visualize them. The proposed retrieval methods weight main diagonal and off-diagonal elements of a CCH in the first and/or the second steps mentioned above. Experiments have shown that the proposed methods with a few bins outperform some conventional methods when large weight is given on off-diagonal elements regardless of color quantization levels. We believe that the effectiveness of the method is caused by the characteristics describing the size and the coherence of homogeneous color regions and being robust to size variation of the color regions. Moreover, the image retrieval performance is little affected by the threshold, which is an energy level of valid bins, regardless of color quantization levels. The proposed methods use contents of images effectively, so they can be effectually used in the other content-based applications such as color image classification, color object tracking, and video cut detection.제１장 서 론 = 1 1.1 연구의 배경 = 1 1.2 제안한 방법 = 3 제２장 내용기반 영상검색을 위한 컬러 기술자 = 6 2.1 내용기반 영상검색 시스템 = 6 2.2 컬러영상을 위한 기술자 = 7 제３장 컬러 동시발생 히스토그램에 의한 영상검색 = 19 3.1 컬러 동시발생 히스토그램의 문제점 = 19 3.2 대각성분과 비대각성분의 영상기술 = 24 3.3 대각성분과 비대각성분의 영상검색 성능 = 29 제４장 가중치를 둔 컬러 동시발생 히스토그램을 이용한 영상검색 = 36 4.1 대각성분 및 비대각성분에 가중치를 둔 영상검색 = 38 4.1.1 대각성분 및 비대각성분에 가중치를 둔 CCH = 38 4.1.2 빈 개수 축소와 유사도 측정 = 42 4.2 대각성분, 비대각성분 및 가중치에 의한 영상검색 = 46 4.2.1 CCH의 획득과 빈 제거 = 46 4.2.2 유사도 측정 = 48 제５장 실험 및 고찰 = 52 5.1 실험환경 및 성능평가 방법 = 52 5.2 실험결과 및 고찰 = 55 제６장 결 론 = 79 참고 문헌 = 8