scholarly journals A Review On Content Based Image Retrieval

2018 ◽  
Vol 17 (2) ◽  
pp. 7226-7235
Author(s):  
Bohar Singh ◽  
Mrs.Mehak Aggarwal
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Image Database ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Visual Images ◽  
Large Database ◽  
Feature Vectors ◽  
Digital Storage ◽  
Similarity Computation

In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s.  To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database.  These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color.  For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing.  Since anautomated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.

scholarly journals COMPREHENSIVE STUDY ON CONTENT BASED IMAGE RETRIEVAL WITH THEIR FEATURES

2017 ◽  
Vol 16 (6) ◽  
pp. 6962-6967
Author(s):  
Pritpal Kaur ◽  
Sukhvir Kaur
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Visual Images ◽  
Large Database ◽  
Feature Vectors ◽  
Digital Storage ◽  
Similarity Computation ◽  
Comprehensive Study

In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s. To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database. These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color. For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing. Since an automated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.

scholarly journals A REVIEW ON MULTIQUERY SYSTEM FOR CONTENT BASED IMAGE RETRIEVAL

2016 ◽  
Vol 15 (13) ◽  
pp. 7342-7346
Author(s):  
Meenu Meenu ◽  
Sonika Jindal
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Retrieval System ◽  
Image Similarity ◽  
Content Based Image Retrieval ◽  
Color Similarity ◽  
Image Retrieval System ◽  
Speed Up ◽  
Similarity Computation ◽  
Main Components

In recent years, very large collections of images and videos have grown rapidly. In parallel with this growth, content-based retrieval and querying the indexed collections are required to access visual information. Two of the main components of the visual information are texture and color. In this thesis, a content-based image retrieval system is presented that computes texture and color similarity among images. Content based image retrieval from large resources has become an area of wide interest now a days in many applications.  To speed up retrieval and similarity computation, the database images are analysed and the extracted regions are clustered according to their feature vectors. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity.

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.

EFFICIENT CONTENT-BASED IMAGE RETRIEVAL USING FUZZY ORGANIZATION AND OPTIMAL RELEVANCE FEEDBACK

2003 ◽  
Vol 03 (01) ◽  
pp. 171-208 ◽  
Author(s):  
ANASTASIOS DOULAMIS ◽  
NIKOLAOS DOULAMIS ◽  
THEODORA VARVARIGOU
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Visual Information ◽  
Information Needs ◽  
Cross Correlation ◽  
Feature Vector ◽  
Fuzzy Classification ◽  
Content Based Image Retrieval ◽  
Visual Content ◽  
Feature Vectors

The performance of a Content-Based Image Retrieval System (CBIR) depends on (a) the system's adaptability to the user's information needs, which permits different types of indexing and simultaneously reduces the subjectivity of human perception for the interpretation of the image visual content and (b) the efficient organization of the extracted descriptors, which represent the rich visual information. Both issues are addressed in this paper. Descriptor organization is performed using a fuzzy classification scheme fragmented into multidimensional classes, instead of the previous works where fuzzy histograms were created in one dimension using, for example, the feature vector norm. Multidimensionality relates the descriptors with one another and thus allows a compact and meaningful visual representation by mapping the elements of the resulted feature vectors with a physical visual interpretation. Furthermore, fuzzy classification is applied for all visual content descriptors, in contrast to the previous approaches where only color information is exploited. Two kinds of content descriptors are extracted in our case; global-based and region-based. The first refers to the global image characteristics, while the second exploits the region-based properties. Regions are obtained by applying a multiresolution implementation of the Recursive Shortest Spanning Tree (RSST) algorithm, called M-RSST in this paper. The second issue is addressed by proposing a computationally efficient relevance feedback mechanism based on an optimal weight updating strategy. The scheme relies on the cross-correlation measure, instead of the Euclidean distance which is mainly used in most relevance feedback algorithms. Cross-correlation is a normalized measure, which expresses how similar the two feature vectors are and thus it indicates a metric of their content similarity. The proposed scheme can be recursively implemented in the case of multiple feedback iterations, instead of the previous approaches. Furthermore, it provides reliable results regardless of the number of selected sample and the feature vector size improving relevance feedback performance, as compared to other approaches.

scholarly journals A Content Based Image Retrieval Model for E-Commerce

2018 ◽  
Vol 7 (``11) ◽  
pp. 24392-24396
Author(s):  
Gibson Kimutai ◽  
Prof. Wilson Cheruiyot ◽  
Dr. Calvins Otieno
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Retrieval Model ◽  
Large Database ◽  
Visual Content ◽  
Color Feature ◽  
Search Results ◽  
Efficient Retrieval ◽  
Database Content

In the last decade, large database of images have grown rapidly. This trend is expected to continue in to the future. Retrieval and querying of these image in efficient way is a challenge in order to access the visual content from large database. Content Based Image Retrieval (CBIR) provides the solution for efficient retrieval of image from these huge image database. Many research efforts have been directed to this area with color feature being the mostly used feature because of its ease of extraction. Although many research efforts have been directed to this area, precision  of majority of the developed models  are still at less than 80%. This is a challenge as it leads to unsatisfying search results. This paper proposes a Content Based Image Retrieval model for E-Commerce.

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.

Retrieval of Multimedia Information Using Content-Based Image Retrieval (CBIR) Techniques

Biometrics ◽  
2017 ◽  
pp. 652-689
Author(s):  
Anupam Mukherjee
Keyword(s):  
Image Retrieval ◽  
Multimedia Information ◽  
Visual Information ◽  
Video Retrieval ◽  
Automatic Generation ◽  
Content Based Image Retrieval ◽  
Manual Labor ◽  
Multidimensional Indexing ◽  
Other Information ◽  
Human Operators

This chapter will focus on the concept of Content-based image retrieval. Searching of an image or video database based on text based description is a manual labor intensive process. Descriptions of the file are usually typed manually for each image by human operators because the automatic generation of keywords for the images is difficult without incorporation of visual information and feature extraction. This method is impractical in today's multimedia information era. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Several important sections are highlighted in this chapter, like architectures, query techniques, multidimensional indexing, video retrieval and different application sections of CBIR.

scholarly journals FEATURE SELECTION USING ASSOCIATION RULES FOR CBIR AND COMPUTER AIDED MEDICAL DIAGNOSTIC

2015 ◽  
pp. 223-227
Author(s):  
C. BHUVANESWARI ◽  
P. ARUNA ◽  
D. LOGANATHAN
Keyword(s):  
Image Retrieval ◽  
Classification Accuracy ◽  
Visual Information ◽  
Naive Bayes ◽  
Diagnostic Techniques ◽  
Modern Medicine ◽  
Naïve Bayes ◽  
Content Based Image Retrieval ◽  
Sudden Increase ◽  
Computer Aided

Digital images are now the basis of visual information in medical applications. The advent of radiology which employs imaging for diagnosis generates great amount of images. Automatic retrieval of images based on features like color, shape and texture is termed Content Based Image Retrieval. The increasing dependence of modern medicine on diagnostic techniques such as radiology, computerized tomography has resulted in a sudden increase in the number and significance of medical images. Content Based Image Retrieval techniques are being extensively used to aid diagnosis by comparing with similar past cases and improvising Computer Aided Diagnosis. In this paper, it is proposed to extract features in the frequency domain using Walsh Hadamard transform and use FP-Growth association rule mining to extract features based on confidence. The extracted features are classified using Naïve Bayes and CART algorithms and the proposed method’s classification accuracy is evaluated. Experimental results show that classification accuracy for Naïve Bayes is 100 and 96.8 for CART on application of proposed method.

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