scholarly journals An Efficient Technique for Image Retrieval from the Large Database on the Basis of Color and Texture

2016 ◽  
Vol 145 (7) ◽  
pp. 6-11
Author(s):  
Mayank Jain ◽  
Divakar Singh
Keyword(s):  
Image Retrieval ◽  
Efficient Technique ◽  
Large Database

A modified Efficient Manifold Ranking Algorithm for Large Database Image Retrieval

2020 ◽  
Vol 23 (1) ◽  
pp. 79-89
Author(s):  
Quy Hoang Van ◽  
Huy Tran Van ◽  
Huy Ngo Hoang ◽  
Tuyet Dao Van ◽  
Sergey Ablameyko
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Ranking Algorithm ◽  
Large Database ◽  
Manifold Ranking ◽  
Fcm Clustering ◽  
Hard Clustering ◽  
Anchor Points ◽  
Large Image Databases

The efficient manifold ranking (EMR) algorithm is used quite effectively in content-based image retrieval (CBIR) for large image databases where images are represented by multiple low-level features to describe about the color, texture and shape. The EMR ranking algorithm requires steps to determine anchor points of the image database by using the k-means hard clustering and the accuracy of the ranking depends strongly on the selected anchor points. This paper describes a new result based on a modified Fuzzy C-Means (FCM) clustering algorithm to select anchor points in the large database in order to increase the efficiency of manifold ranking specially for the large database cases. Experiments have demonstrated the effectiveness of the proposed algorithm for the issue of building an anchor graph, the set of anchor points determined by this novel lvdc-FCM algorithm has actually increased the effective of manifold ranking and the quality of images query results which retrieved of the CBIR.

scholarly journals A Survey on Features and Techniques in Content Based Image Retrieval

2015 ◽  
Vol 14 (10) ◽  
pp. 6129-6134
Author(s):  
Meenu Kalra ◽  
Pooja Handa
Keyword(s):  
Image Retrieval ◽  
Digital Images ◽  
Content Based Image Retrieval ◽  
Rapid Rate ◽  
Human Intervention ◽  
Large Database ◽  
Widespread Application ◽  
Retrieval Systems

Content-based image retrieval (CBIR) is widely adopted method for finding images from vast collection of images in the database. As the collections of images are growing at a rapid rate, demand for efficient and effective tools for retrieval of query images from database is increased significantly. Among them, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images as it requires relatively less human intervention. The requirement for development of CBIR is enhanced due to tremendous growth in volume of images as well as the widespread application in multiple fields. Texture, color, shaped, contours etc are the important entities to represent and search the images. These features of images are extracted and implemented for a similarity check among images. In this paper, we have conducted a survey on the CBIR techniques and its approaches and their usage in various domains.

scholarly journals Cloud-Based Image Retrieval Using GPU Platforms

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 48 ◽  
Author(s):  
Sidi Ahmed Mahmoudi ◽  
Mohammed Amin Belarbi ◽  
El Wardani Dadi ◽  
Saïd Mahmoudi ◽  
Mohammed Benjelloun
Keyword(s):  
Image Retrieval ◽  
Performance Improvement ◽  
Computation Time ◽  
Image Features ◽  
Multimedia Retrieval ◽  
Large Database ◽  
Computation Complexity ◽  
Multimedia Documents ◽  
Large Databases ◽  
Reduction Of Energy Consumption

The process of image retrieval presents an interesting tool for different domains related to computer vision such as multimedia retrieval, pattern recognition, medical imaging, video surveillance and movements analysis. Visual characteristics of images such as color, texture and shape are used to identify the content of images. However, the retrieving process becomes very challenging due to the hard management of large databases in terms of storage, computation complexity, temporal performance and similarity representation. In this paper, we propose a cloud-based platform in which we integrate several features extraction algorithms used for content-based image retrieval (CBIR) systems. Moreover, we propose an efficient combination of SIFT and SURF descriptors that allowed to extract and match image features and hence improve the process of image retrieval. The proposed algorithms have been implemented on the CPU and also adapted to fully exploit the power of GPUs. Our platform is presented with a responsive web solution that offers for users the possibility to exploit, test and evaluate image retrieval methods. The platform offers to users a simple-to-use access for different algorithms such as SIFT, SURF descriptors without the need to setup the environment or install anything while spending minimal efforts on preprocessing and configuring. On the other hand, our cloud-based CPU and GPU implementations are scalable, which means that they can be used even with large database of multimedia documents. The obtained results showed: 1. Precision improvement in terms of recall and precision; 2. Performance improvement in terms of computation time as a result of exploiting GPUs in parallel; 3. Reduction of energy consumption.

Comparison of deep learning techniques on content based image retrieval

2019 ◽  
pp. 1950285 ◽  
Author(s):  
Meenakshi Garg ◽  
Manisha Malhotra ◽  
Harpal Singh
Keyword(s):  
Image Retrieval ◽  
Medical Diagnosis ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Fuzzy Classification ◽  
Content Based Image Retrieval ◽  
Large Database ◽  
Fuzzy Classifier ◽  
Learning Techniques ◽  
Neuro Fuzzy

Content based Image Retrieval is mostly utilized to extract the pictures from large database. CBIR, which is additionally known as “Query by image” is a technology allowing organizing the computerized Images by their visible attributes. Or, in other words, CBIR is a method for retrieving pictures dependent not on annotations or keywords, but dependent on the feature taken out straightly from the pictures database. CBIR systems are dependent on the utilizations of computer vision methods to the image retrieval issue in huge databases. CBIR is technology of recovering the utmost visually identical pictures to a specified query picture from a cluster or database of pictures. It is useful in a lot of areas like Photography to search images from the database, medical diagnosis etc. Physically annotating the pictures by inputting the metadata or keywords in a huge database can be laborious and might not capture the keyword anticipated to define that picture. CBIR supports in recovering identical pictures from an database of pictures deprived of pictures annotation. In this paper, we are compare the Deep Neural Networks and Neuro-Fuzzy Classifier, both have different outcomes and different results to predict the image. The comparison of our proposed methods Neuro-fuzzy classification and deep neural network shows that the improvement in accuracy. The accuracy values 71.6% and 73.4% for DNN and Neuro-Fuzzy Classifier[Formula: see text] method. The visual and qualitative results are presented for validation of the proposed method.

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 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.

Multimodal Image Retrieval over a Large Database

2010 ◽  
pp. 177-184 ◽  
Author(s):  
Débora Myoupo ◽  
Adrian Popescu ◽  
Hervé Le Borgne ◽  
Pierre-Alain Moëllic
Keyword(s):  
Image Retrieval ◽  
Large Database

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.

Sign in / Sign up

Export Citation Format

Share Document