scholarly journals Image Retrieval Based on WBCH and Clustering Algorithm

2013 ◽  
Vol 5 (3) ◽  
pp. 604-613
Author(s):  
Asmita Bhaskar Shirsath ◽  
M. J. Chouhan ◽  
N. J Uke
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Similarity Measures ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Shape Information ◽  
Image Descriptors ◽  
Image Retrieval System ◽  
Retrieval Result

Research on content-based image retrieval has gained tremendous momentum during the last decade. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. In order to get faster  retrieval result from large-scale image database ,we proposed image retrieval system in which image database is first pre-processed by Wavelet Based Color Histogram (WBCH) and K-means algorithm and then using Hierarchical clustering algorithm we index the previous result and then by using similarity measures we retrieve the images from pre-processed database. Experiments show that this proposed method offers substantial increase in retrieval speed but needs to be improved on retrieval results.

Content Based Image Retrieval Using Active-Nets

2011 ◽  
pp. 85-114 ◽  
Author(s):  
David García Pérez ◽  
Antonio Mosquera ◽  
Stefano Berretti ◽  
Alberto Del Bimbo
Keyword(s):  
Image Retrieval ◽  
Structural Information ◽  
Similarity Measures ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Objects ◽  
Image Retrieval System ◽  
Deformable Structure ◽  
Retrieval Problem ◽  
Active Research

Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of retrieval, but the large part of these works have focused on sub-parts of the retrieval problem, providing targeted solutions only for individual aspects (i.e., feature extraction, similarity measures, indexing, etc). In this chapter, we first shortly review some of the main practiced solutions for content-based image retrieval evidencing some of the main issues. Then, we propose an original approach for the extraction of relevant image objects and their matching for retrieval applications, and present a complete image retrieval system which uses this approach (including similarity measures and image indexing). In particular, image objects are represented by a two-dimensional deformable structure, referred to as “active net.” Active net is capable of adapting to relevant image regions according to chromatic and edge information. Extension of the active nets has been defined, which permits the nets to break themselves, thus increasing their capability to adapt to objects with complex topological structure. The resulting representation allows a joint description of color, shape, and structural information of extracted objects. A similarity measure between active nets has also been defined and used to combine the retrieval with an efficient indexing structure. The proposed system has been experimented on two large and publicly available objects databases, namely, the ETH-80 and the ALOI.

scholarly journals Towards Large Scale Image Retrieval System Using Parallel Frameworks

2020 ◽  
Author(s):  
Saliha Mezzoudj
Keyword(s):  
Big Data ◽  
Image Retrieval ◽  
Large Scale ◽  
Retrieval System ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Content Search ◽  
Parallel Frameworks ◽  
Image Retrieval System ◽  
Large Scale Image Retrieval

Recently, the increasing use of mobile devices, such as cameras and smartphones, has resulted in a dramatic increase in the amount of images collected every day. Therefore, retrieving and managing these large volumes of images has become a major challenge in the field of computer vision. One of the solutions for efficiently managing image databases is an Image Content Search (CBIR) system. For this, we introduce in this chapter some fundamental theories of content-based image retrieval for large scale databases using Parallel frameworks. Section 2 and Section 3 presents the basic methods of content-based image retrieval. Then, as the emphasis of this chapter, we introduce in Section 1.2 A content-based image retrieval system for large-scale images databases. After that, we briefly address Big Data, Big Data processing platforms for large scale image retrieval. In Sections 5, 6, 7, and 8. Finally, we draw a conclusion in Section 9.

A Multi-resolution Approach for Content-based Image Retrieval on the Grid

2005 ◽  
Vol 44 (02) ◽  
pp. 211-214 ◽  
Author(s):  
T. Tweed ◽  
S. Miguet ◽  
K. Hassan
Keyword(s):  
Image Retrieval ◽  
High Performance ◽  
Large Scale ◽  
Breast Cancer Diagnosis ◽  
Content Based Image Retrieval ◽  
Distributed Data ◽  
Promising Tool ◽  
Medical Centers ◽  
Image Retrieval System ◽  
Processing Resources

Summary Objectives: Hospitals and medical centers are producing more and more data that need to be processed. Those data are confidential, heterogeneous, and limited to the geographic site where they have been produced. Unless properly anonymized, they cannot be distributed on wide area networks. Methods: Grid technologies allow the globalization of storage and processing resources, and enable large-scale experimentations on distributed data. They constitute a promising tool to treat the different data and analyze the knowledge they contain, while offering secured access and high-performance computing capacities to the different users. Our aim is to evaluate the possibilities of grid technologies for handling medical data. Results and Conclusions: In this paper, we focus on a breast cancer diagnosis assistance tool, based on distributed and incremental knowledge construction and a content-based image retrieval system. We analyze the different scenarios of uses of such a tool. We further propose an algorithm that indexes mammographic images for content-based query purposes. This algorithm is tested on images of different resolutions in order to reduce the indexation time and we analyze its performance with experiments on the grid.

Fuzzy Techniques for Content-Based Image Retrieval

2018 ◽  
pp. 41-64
Author(s):  
Rose Bindu Joseph P. ◽  
Ezhilmaran Devarasan
Keyword(s):  
Image Retrieval ◽  
Fuzzy Theory ◽  
Spatial Relationship ◽  
Similarity Measures ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Human Reasoning ◽  
Fuzzy Distance ◽  
Image Retrieval System ◽  
Fuzzy Techniques

Content-based image retrieval aims to acquire images from huge databases by analyzing their visual features like color, texture, shape, and spatial relationship. The search for superior accuracy in image retrieval has resulted in concentrating more on semantic gap reduction between the low-level features and high level human reasoning. Fuzzy theory is a prevailing methodology which helps in attaining this goal by using attributes and interpretations similar to human reasoning. The vagueness and impreciseness in image data and the retrieval process can be modeled by fuzzy sets. This chapter analyses fuzzy theoretic approaches in various stages of content-based image retrieval system. Various fuzzy-based feature descriptors are discussed along with different fuzzy classification and indexing algorithms for content-based image retrieval. This chapter also presents an overview of various fuzzy distance and similarity measures for image retrieval. A novel fuzzy theoretic retrieval for finger vein biometric images is also proposed in this chapter with experiment and analysis.

scholarly journals Content-Based Image Retrieval System for Real Images

10.29007/w4sr ◽  
2018 ◽  
Author(s):  
Yin-Fu Huang ◽  
Bo-Rong Chen
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
Multimedia Data ◽  
Content Based Image Retrieval ◽  
Real Image ◽  
Query Image ◽  
Image Retrieval System ◽  
Similar Images ◽  
Network Technologies

With the rapid progress of network technologies and multimedia data, information retrieval techniques gradually become content-based, and not text-based yet. In this paper, we propose a content-based image retrieval system to query similar images in a real image database. First, we employ segmentation and main object detection to separate the main object from an image. Then, we extract MPEG-7 features from the object and select relevant features using the SAHS algorithm. Next, two approaches “one-against- all” and “one-against-one” are proposed to build the classifiers based on SVM. To further reduce indexing complexity, K-means clustering is used to generate MPEG-7 signatures. Thus, we combine the classes predicted by the classifiers and the results based on the MPEG-7 signatures, and find out the similar images to a query image. Finally, the experimental results show that our method is feasible in image searching from the real image database and more effective than the other methods.

scholarly journals Unsupervised Content Based Image Retrieval by Combining Visual Features of an Image With A Threshold

2012 ◽  
pp. 149-154
Author(s):  
S. M. Zakariya ◽  
Rashid Ali ◽  
Nesar Ahmad
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Query Image ◽  
System A ◽  
Image Retrieval System

Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape and texture to represent and index the image. In a typical content based image retrieval system, a set of images that exhibit visual features similar to that of the query image are returned in response to a query. CLUE (CLUster based image rEtrieval) is a popular CBIR technique that retrieves images by clustering. In this paper, we propose a CBIR system that also retrieves images by clustering just like CLUE. But, the proposed system combines all the features (shape, color, and texture) with a threshold for the purpose. The combination of all the features provides a robust feature set for image retrieval. We evaluated the performance of the proposed system using images of varying size and resolution from image database and compared its performance with that of the other two existing CBIR systems namely UFM and CLUE. We have used four different resolutions of image. Experimentally, we find that the proposed system outperforms the other two existing systems in ecery resolution of image.

scholarly journals A Review on LBP in Image Retrieval System for Future Enhancement and Vector Images

2020 ◽  
pp. 155-161
Author(s):  
K Rajalakshmi ◽  
V Krishna Dharshini ◽  
S Selva Meena
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Query Image ◽  
Matching Algorithm ◽  
Image Retrieval System ◽  
Similar Images ◽  
Vector Images ◽  
Similar Kind

Content-Based Image Retrieval is a process to retrieve the similar images from the large set of image database corresponding to the query image. In CBIR low level or pixel level features such as color, texture and shape of the images are extracted and on the basis of similarity matching algorithm the required similar kind of images are retrieved from the image database. To understand the evaluation and evolution of CBIR system various research was studied and various research is going on this way also. In this paper, we have discussed some of the popular pixel level feature extraction techniques for Content-Based Image Retrieval and we also present here about the performance of each technique.

Approaches for Image Database Retrieval Based on Color, Texture, and Shape Features

2017 ◽  
pp. 28-50
Author(s):  
Kratika Arora ◽  
Ashwani Kumar Aggarwal
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Extraction Methods ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Good Precision ◽  
Retrieval Systems ◽  
Main Motive ◽  
Image Retrieval System ◽  
Active Research

With an ever-increasing use and demand for digital imagery in the areas of medicine, sciences, and engineering, image retrieval is an active research area in image processing and pattern recognition. Content-based image retrieval (CBIR) is a method of finding images from a huge image database according to persons' interests. Content-based here means that the search involves analysis of the actual content present in the image. As the database of images is growing day by day, researchers/scholars are searching for better techniques for retrieval of images with good efficiency.This chapter first gives an overview of the various image retrieval systems. Then, the applications of CBIR in various fields and existing CBIR systems are described. The various image content descriptors and extraction methods are also explained. The main motive of the chapter is to study and compare the features that are used in Content Based Image Retrieval system and conclude on the system that retrieves images from a huge database with good precision and recall.

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