scholarly journals A Review on Content Based Image Retrieval System Features derived by Deep Learning Models

2021 ◽  
Vol 9 (12) ◽  
pp. 42-57
Author(s):  
Mr. Kommu Naveen
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Network Models ◽  
Classification Problem ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Usual Procedure ◽  
Image Retrieval System ◽  
High Level ◽  
Trained Network

Abstract: In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image, and retrieve images which have similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally the choice of these features play a very important role in the success of this system, and high level features are required to reduce the “semantic gap”. In this paper, we propose to use features derived from pre-trained network models from a deep- learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method, and also propose a pre-clustering of the database based on the above-mentioned features which yields comparable results in a much shorter time in most of the cases. Keywords Content Based Image Retrieval Feature Selection Deep Learning Pre-trained Network Models Pre-clustering

scholarly journals CBIR Using Features Derived by Deep Learning

2021 ◽  
Vol 2 (3) ◽  
pp. 1-24
Author(s):  
Subhadip Maji ◽  
Smarajit Bose
Keyword(s):  
Deep Learning ◽  
Network Models ◽  
Classification Problem ◽  
Semantic Gap ◽  
Large Database ◽  
High Similarity ◽  
Query Image ◽  
Usual Procedure ◽  
Similar Images ◽  
High Level

In a Content-based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image and retrieve images that have a similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally, the choice of these features play a very important role in the success of this system, and high-level features are required to reduce the “semantic gap.” In this article, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method and also propose a pre-clustering of the database based on the above-mentioned features, which yields comparable results in a much shorter time in most of the cases.

scholarly journals Combined global and local semantic feature–based image retrieval analysis with interactive feedback

2019 ◽  
Vol 53 (1-2) ◽  
pp. 3-17
Author(s):  
A Anandh ◽  
K Mala ◽  
R Suresh Babu
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Research Work ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Retrieval System ◽  
False Image ◽  
Active Research ◽  
High Level ◽  
Interactive Feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

Downsizing the Semantic Gap in Contextual Image Retrieval System Using Superintend Gross Silhouette Descriptor

2020 ◽  
Vol 8 (4) ◽  
pp. 1-20
Author(s):  
Girija G. Chiddarwar ◽  
S.Phani Kumar
Keyword(s):  
Image Retrieval ◽  
Correlation Coefficient ◽  
Retrieval System ◽  
Shape Descriptor ◽  
Query Image ◽  
Relationship Strength ◽  
Image Retrieval System ◽  
High Level ◽  
Straight Lines ◽  
The Relationship

Since shape is the most important feature for recognizing objects, it has to be extracted accurately in order to enhance the content based image retrieval system, but challenges prevailed in extracting shape features of an object in an image due to inability of shape descriptor which extracts a limited number of different shapes that are not invariant, alongside the inability to extracting features of overlapping objects, and the shape connotation gap problem between low level and high level features. In order to overcome these problems, this work proposes a Superintend Gross Silhouette Descriptor which uses pixel coordinates on spatial domain of the image for finding the real shape of the object by means of straight lines so it has the ability to detect the overlapped objects as well as the polygonal shapes. After being extracted, features would be trained using a random woodland classifier which classifies the features into a group of classes at maximum convergence for mitigating the shape connotation problem. At the time of retrieval, the features of the query image would be tested with trained features for measuring the similarity by the dynamite correlation coefficient method, which is a measure of the linear correlation so it would render the absolute value of the correlation coefficient which maintains the relationship strength among features.

scholarly journals Content Based Image Retrieval (CBIR) by Statistical Methods

2020 ◽  
Vol 17 (2(SI)) ◽  
pp. 0694
Author(s):  
Fathala Ali et al.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Semantic Content ◽  
T Test ◽  
Statistical Characteristics ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Image Retrieval System ◽  
Level Of Significance ◽  
Second Procedure

            An image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T- test to measure the independence between more than images, (coefficient of correlate, T- test, Level of significance, find the decision), and, through experimental test, it was found that this proposed method of retrieval technique is powerful than the classical retrieval System.

A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

2019 ◽  
pp. 54-64
Author(s):  
Mohamed Elsharkawy ◽  
◽  
Ahmed N. Al Masri ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Image Encryption ◽  
Cuckoo Search ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Search Optimization ◽  
Proposed Model ◽  
Cuckoo Search Optimization ◽  
Visual Properties

From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over the other methods.

Content-based Image Retrieval by Information Theoretic Measure

2011 ◽  
Vol 61 (5) ◽  
pp. 415 ◽  
Author(s):  
Madasu Hanmandlu ◽  
Anirban Das
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Texture Features ◽  
Entropy Function ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Information Theoretic ◽  
Dominant Colour ◽  
Image Retrieval System ◽  
Information Theoretic Measures

<p>Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.415-430</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1177</strong></strong></p>

scholarly journals Content-Based Image Retrieval : A Comprehensive Study

2019 ◽  
pp. 1073-1081
Author(s):  
Er Aman ◽  
Amit Rawat ◽  
Ashwin Giri ◽  
Hardik Gothwal
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Empirical Studies ◽  
Content Based Image Retrieval ◽  
Recent Success ◽  
Long Run ◽  
Learning Techniques ◽  
Depth Analysis ◽  
High Level ◽  
Learning Options

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.

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.

Sign in / Sign up

Export Citation Format

Share Document