scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine

TL;DR: CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work, and Superiority of the proposed CBIR system is observed over other moment based methods in terms of retrieval efficiency and retrieval time.
Abstract: Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Therefore, an efficient and orthogonal moment based CBIR system is needed. Legendre Moments (LM) are orthogonal, computationally faster, and can represent image shape features compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work. Superiority of the proposed CBIR system is observed over other moment based methods, viz., MI and ZM in terms of retrieval efficiency and retrieval time. Further, the classification efficiency is improved by employing Support Vector Machine (SVM) classifier. Improved retrieval results are obtained over existing CBIR algorithm based on Stacked Euler Vector (SERVE) combined with Modified Moment Invariants (MMI).

Content maybe subject to copyright    Report

Citations
More filters
01 Jan 2014
TL;DR: Various content-based image retrieval techniques available for retrieving the require and classify images are reviewed, and some basic features of any image, like shape, texture, color, are shown and different techniques to calculate them are shown.
Abstract: Various content-based image retrieval techniques are available for retrieving the require and classify images, we are reviewing them. In our first section, we are tending towards some basics of a particular CBIR system with that we have shown some basic features of any image, these are like shape, texture, color and shown different techniques to calculate them. In the next section, we have shown different distance measuring techniques used for similarity measurement of any image and also discussed indexing techniques. Finally conclusion and future scope is discussed.

81 citations


Cites background from "Content Based Image Retrieval Using..."

  • ...normalized pixel coordinates, and are given by equation (26) [24][40]....

    [...]

01 Jan 2013
TL;DR: This survey covers approaches used for extracting low level features; various distance measures for measuring the similarity of images, the mechanisms for reducing the semantic gap and about invariant image retrieval.
Abstract: Content Based Image Retrieval (CBIR) is a very important research area in the field of image processing, and comprises of low level feature extraction such as color, texture and shape and similarity measures for the comparison of images. Recently, the research focus in CBIR has been in reducing the semantic gap, between the low level visual features and the high level image semantics. This paper provides a comprehensive survey of all these aspects. This survey covers approaches used for extracting low level features; various distance measures for measuring the similarity of images, the mechanisms for reducing the semantic gap and about invariant image retrieval. In addition to these, various data sets used in CBIR and the performance measures, are also addressed. Finally, future research directions are also suggested. (I. Felci Rajam, S. Valli. A Survey on Content Based Image Retrieval. Life Sci J 2013; 10(2): 2475-2487). (ISSN: 1097-8135). http://www.lifesciencesite.com 343

45 citations

Journal ArticleDOI
TL;DR: This work proposes a graph matching scheme that involves color, texture and shape features along with spatial descriptors to represent topological and orientation/directional relationships—which are obtained by means of combinatorial pyramids—in order to identify similar objects from a database.
Abstract: Spatial relations among objects and object parts play a fundamental role in the human perception and understanding of images, thus becoming very relevant in the computational fields of object recognition, scene understanding and content-based image retrieval. In this work we propose a graph matching scheme that involves color, texture and shape features along with spatial descriptors to represent topological and orientation/directional relationships--which are obtained by means of combinatorial pyramids--in order to identify similar objects from a database. We also suggest a method for deciding which are the more useful levels in the hierarchy of segmentation for the recognition process. Our main objective is to prove that the combination of visual and spatial features is a promising road in order to improve the object recognition task. We performed experiments on two well known databases, COIL-100 and ETH-80 image sets, in order to evaluate the expressiveness of the proposed representation. These sets introduce challenges for simple object recognition in terms of view-point changes, and our results were comparable or superior than other state-of-the-art methods.

30 citations


Cites methods from "Content Based Image Retrieval Using..."

  • ...They also have been successfully used for object classification [1] and image retrieval [37]....

    [...]

Journal ArticleDOI
01 Jan 2020
TL;DR: The comparison clearly shows that the proposed algorithm outperforms the existing compression algorithms in terms of mean square error, peak signal-to-noise ratio, normalized correlation coefficient, and structural similarity index.
Abstract: Volumetric medical images are widely used in diagnosing and detecting health problems of patients. Large datasets of volumetric medical images required huge storage space and high network capabilities to transmit these medical images from one location to another especially in the applications of telemedicine and teleradiology. In addition, the quality of medical images plays an important role in successful diagnoses. Therefore, an efficient compression algorithm must achieve significant reduction in the size of these volumetric medical images by using high compression ratio and preserve the quality of these images for successful diagnosis. In this paper, a novel optimized compression algorithm for volumetric medical images is proposed. In this algorithm, the volumetric medical images are divided into two-dimensional (2D) slices where each slice is divided into a group of 8 × 8 nonoverlapped blocks. The Legendre moments are computed for each block where the differential evolution optimization algorithm is utilized to select the optimum moments according to minimization of the cost function. Volumetric medical images from different medical imaging modalities are used in testing and evaluating the proposed compression algorithm. The performance of the proposed algorithm is compared with the existing volumetric medical images compression algorithms where the comparison clearly shows that the proposed algorithm outperforms the existing compression algorithms in terms of mean square error, peak signal-to-noise ratio, normalized correlation coefficient, and structural similarity index.

25 citations

Journal ArticleDOI
TL;DR: The results show the accuracy of the new set of Gegenbauer moment invariants, expressed as a linear combination of geometric moment invariant where the later are invariants under translation, scaling and rotation of the image they describe.
Abstract: A new set of Gegenbauer moment invariants is proposed for pattern recognition applications. These moment invariants are expressed as a linear combination of geometric moment invariants where the later are invariants under translation, scaling and rotation of the image they describe. The invariance of Gegenbauer moments is tested by using different binary- and gray-level images. The obtained results show the accuracy of the new set of Gegenbauer moment invariants.

21 citations


Cites methods from "Content Based Image Retrieval Using..."

  • ...[10] used exact Legendre moments and support vector machine in contentbased image retrieval....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

15,696 citations

Journal ArticleDOI
Ming-Kuei Hu1
TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Abstract: In this paper a theory of two-dimensional moment invariants for planar geometric figures is presented. A fundamental theorem is established to relate such moment invariants to the well-known algebraic invariants. Complete systems of moment invariants under translation, similitude and orthogonal transformations are derived. Some moment invariants under general two-dimensional linear transformations are also included. Both theoretical formulation and practical models of visual pattern recognition based upon these moment invariants are discussed. A simple simulation program together with its performance are also presented. It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished. It is also indicated that generalization is possible to include invariance with parallel projection.

7,963 citations


"Content Based Image Retrieval Using..." refers background in this paper

  • ...10.5121/ijma.2010.2206 69 KEYWORDS CBIR, LM, ELM, Feature Extraction, Support Vector Machine....

    [...]

  • ...Image moments and their functions have been utilized as features in many image processing applications, viz., pattern recognition, image classification, target identification, and shape analysis....

    [...]

  • ...Moments of an image are treated as region-based shape descriptors....

    [...]

Journal ArticleDOI
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Abstract: Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

6,447 citations


Additional excerpts

  • ...10.5121/ijma.2010.2206 69 KEYWORDS CBIR, LM, ELM, Feature Extraction, Support Vector Machine....

    [...]

Journal ArticleDOI
TL;DR: The Query by Image Content (QBIC) system as discussed by the authors allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information.
Abstract: Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. Two key properties of QBIC are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. This article describes the QBIC system and demonstrates its query capabilities. QBIC technology is part of several IBM products. >

3,957 citations

Journal ArticleDOI
TL;DR: Almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation are surveyed, and the spawning of related subfields are discussed, to discuss the adaptation of existing image retrieval techniques to build systems that can be useful in the real world.
Abstract: We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

3,433 citations