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

Reconhecimento de padrões usando indexação recursiva

Tese (Doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico

Image database retrieval using neural networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The broad objective of this work has been to achieve retrieval of images from large unconstrained databases using image content. The problem is typified by the need to locate a target image within a database where no numerical indexing terms exist. Here, retrieval is based on important features within in an image and uses sample images or user sketches to specify a query. A typical query might be framed as "Find all images similar to this one", for example. The aim of this work has been to show how neural networks can provide a practical, flexible and robust solution to this problem. A neural network is basically an adaptive information filter which can be used to extract the salient characteristics of a data set during a training phase. The transformation learnt by the network can map the images into compact indices which support very rapid fuzzy matching of images across the database. This learning process optimises the performance of the code with respect to the contents of the database. We assess the applicability of several neural network architectures and learning rules for a practical coding scheme and investigate how the system parameters affect the performance of the system. We introduce a novel learning law which has a number of advantages over existing paradigms. In-depth mathematical analysis and extensive empirical tests are used to corroborate the arguments presented throughout. This thesis aims to show the nature of the image retrieval problem, how current research trends attempt to tackle it and how neural networks can offer us a real alternative to conventional approaches

Design and evaluation of a shape retrieval system

PhD ThesisWhile automated storage and retrieval systems for textual and numeric data are now commonplace, the development of analogous systems for pictorial data has lagged behind - not through the lack of need for such systems, but because their development involves a number of significant problems. The aim of this project is to investigate these problems by designing and evaluating an information retrieval system for a specific class of picture, 2-dimensional engineering drawings. This involves consideration of the retrieval capabilities needed by such· a system, what storage structures it would require, how the salient features of each drawing should be represented, how query and stored shapes should be matched, what features were of greatest importance in retrieval, and the interfaces necessary to formulate queries and display results. A form of hierarchical boundary representation has been devised for stored shapes, in which each boundary can be viewed as a series of levels of steadily increasing complexity. A set of rules for boundary and segment ordering ensures that as far as possible, each shape has a unique representation. For each level at which each boundary can be viewed, a set of invariant shape features characterizing that level is extracted and added to the shape representation stored in the database. Two classes of boundary feature have been defmed; global features, characteristic of the boundary as a whole, and local features, corresponding to individual fragments of the boundary. To complete the shape description, position features are also computed and stored, to specify the pattern of inner boundaries within the overall shape. Six different tYPes of shape retrieval have been distinguished, although the prototype system can offer only three of these - exact shape matching, partial shape matching and similarity matching. Complete or incomplete query shapes can be built up at a terminal, and subjected to a feature extraction process similar to that for stored drawings, yielding a query fIle that can be matched against the shape database. A variety of matching techniques is provided, including similarity estimation using global or local features, tests for the existence of specified local features in stored drawings, and cumulative angle vs distance matching between query and stored shape boundaries. Results can be displayed in text or graphical form. The retrieval performance of the system in similarity matching mode has been evaluated by comparing its rankings of shapes retrieved in response to test queries with those obtained by a group of human subjects faced with the same task. Results, expressed as normalized recall and precision, are encouraging, particularly for similarity estimation using either global or local boundary features. While the detailed results are of limited significance until confrrmed with larger test collections, they appear sufficiently promising to warrant the development of a more advanced prototype capable of handling 3-D geometric models. Some design aspects of the system would appear to be applicable to a wider range of pictorial information systems