Comparison of Color, Texture and ICM Features in CBIR System

2011 ◽  
Vol 403-408 ◽  
pp. 13-19 ◽  
Author(s):  
Sonali Bhadoria ◽  
Meenakshi Madugunki ◽  
C.G. Dethe ◽  
Preeti Aggarwal
Keyword(s):  
Computer Vision ◽  
Image Retrieval ◽  
Data Base ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Retrieval Process ◽  
Retrieval Performance ◽  
Research Areas ◽  
Matching Techniques ◽  
Index Search

Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. There are various features which can be extracted from the image which gives different performance in retrieving the image.al systems. In this paper we have tried to compare the effect of using different features on the same data base to implement CBIR system. We have tried to analyse the retrieval performance for each feature. We have compared different features as well as the combinations of them to improve the performance. We have also compared the effect of different matching techniques on the retrieval process.

Evaluation of Light Inspired Optimization Algorithm-Based Image Retrieval

2014 ◽  
Vol 573 ◽  
pp. 529-536
Author(s):  
T. Kanimozhi ◽  
K. Latha
Keyword(s):  
Image Retrieval ◽  
Firefly Algorithm ◽  
Retrieval System ◽  
Computation Time ◽  
Image Databases ◽  
Image Search ◽  
Retrieval Process ◽  
Large Collection ◽  
Image Retrieval System ◽  
Unique Method

Image retrieval system becoming a more popular in all the disciplines of image search. In real-time, interactive image retrieval system has become more accurate, fast and scalable to large collection of image databases. This paper presents a unique method for an image retrieval system based on firefly algorithm, which improve the accuracy and computation time of the image retrieval system. The firefly algorithm is utilized to optimize the image retrieval process via search for nearly optimal combinations between the corresponding features as well as finding out approximate optimized weights for similarities with respect to the features. The proposed method is able to dynamically reflect the user’s intention in the retrieval process by optimizing the objective function. The Efficiency of the proposed method is compared with other existing image retrieval methods through precision and recall. The performance of the method is experimented on the Corel and Caltech database images.

A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

2018 ◽  
Vol 45 (1) ◽  
pp. 117-135 ◽  
Author(s):  
Amna Sarwar ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
Khurram Ashfaq Qazi ◽  
Ahmed Adnan ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Image Databases ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Bag Of Words ◽  
Visual Words ◽  
Visual Attributes ◽  
Visual Vocabulary ◽  
Novel Method

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

scholarly journals Image Retrieval Based on Fractal Dictionary Parameters

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Yuanyuan Sun ◽  
Rudan Xu ◽  
Lina Chen ◽  
Xiaopeng Hu
Keyword(s):  
Computer Vision ◽  
Image Compression ◽  
Image Retrieval ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Efficient Management ◽  
Retrieval Rate ◽  
Statistical Indices ◽  
Visual Database

Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods.

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.

scholarly journals Partitioning Medical Image Databases for Content-based Queries on a Grid

2005 ◽  
Vol 44 (02) ◽  
pp. 154-160 ◽  
Author(s):  
V. Breton ◽  
I. E. Magnin ◽  
J. Montagnat
Keyword(s):  
Theoretical Model ◽  
Image Retrieval ◽  
Medical Image ◽  
Computation Time ◽  
Image Database ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Medical Databases ◽  
Grid Nodes ◽  
The Impact

Summary Objectives: In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. Methods: A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. Results: We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Conclusions: Grids are promising for content-based image retrieval in medical databases.

Concepts of Emergence Index in Image Databases

2005 ◽  
pp. 519-522
Author(s):  
Sagarmay Deb
Keyword(s):  
Image Retrieval ◽  
Database Management ◽  
Weather Forecasting ◽  
Video Camera ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Fashion Design ◽  
Art Galleries ◽  
Scientific Database ◽  
X Ray

Images are generated everywhere from various sources. It could be satellite pictures, biomedical, scientific, entertainment, sports and many more, generated through video camera, ordinary camera, x-ray machine and so on. These images are stored in image databases. Content-based image retrieval (CBIR) technique is being applied to access these vast volumes of images from databases efficiently. Some of the areas where CBIR is applied are weather forecasting, scientific database management, art galleries, law enforcement and fashion design.

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