scholarly journals Application of content based image retrieval in digital image search system

2021 ◽  
Vol 10 (2) ◽  
pp. 1122-1128
Author(s):  
Syamsul Yakin ◽  
Tasrif Hasanuddin ◽  
Nia Kurniati
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Image Data ◽  
Multimedia Data ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Image Search ◽  
Image Description ◽  
Search System ◽  
Query Image

Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage of the CBIR method is automatic retrieval process, compared to traditional keyword. This research was conducted on a combination of the HSV color histogram methods and the discrete wavelet transform to extract color features and textures features, while the chi-square distance technique was used to compare the test images with images into a database. The results have showed that the digital image search system with color and texture features have a precision value of 37.5% - 100%, with an average precision value of 80.71%, while the percentage accuracy is 93.7% - 100% with an average accuracy is 98.03%.

Content-Based Image Retrieval Query Paradigms

2005 ◽  
pp. 556-563 ◽  
Author(s):  
Colin C. Venters ◽  
Richard J. Hartley ◽  
William T. Hewitt
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Image Data ◽  
Data Capture ◽  
Content Based Image Retrieval ◽  
Spatial Relationships ◽  
Classification Schemes ◽  
Technological Advances ◽  
Retrieval Problem ◽  
And Storage

The proliferation in volume of digital image data has exacerbated the general image retrieval problem, creating a need for efficient storage and flexible retrieval of vast amounts of image data (Chang, 1989). Whilst there have been significant technological advances with image data capture and storage, developments in effective image retrieval have not kept pace. Research in image retrieval has been divided into two areas: concept-based image retrieval and content-based image retrieval. The former focuses on the use of classification schemes or indexing terms to retrieve images while the latter focuses on the visual features of the image, such as colour, shape, texture, and spatial relationships.

scholarly journals An Efficient Two-layer based Technique for Content-based Image Retrieval

2021 ◽  
Vol 5 (1) ◽  
pp. 28
Author(s):  
Fawzi Abdul Azeez Salih ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Color Spaces ◽  
Query Image ◽  
Current State ◽  
Similar Images ◽  
Different Color

The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.

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 CONTENT BASED IMAGE RETRIEVAL USING MULTI-SVM TECHNIQUE FROM NOISY AND NON NOISY IMAGES.

2020 ◽  
Vol 9 (10) ◽  
pp. 49-52
Keyword(s):  
Image Retrieval ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Mining Machine ◽  
Image Mining ◽  
Query Image ◽  
Processing Data ◽  
Average Accuracy ◽  
Information Image ◽  
New System

Image mining is a technique which handles the mining of information, image data association, or additional patterns not unambiguously stored in the images. It exploits methods from computer vision, image retrieval, image processing, data mining, machine learning, database, and artificial intelligence. In the proposed work, we have developed a new system that can retrieve the images from a dataset on the basis of contents of the query image. Here, ‘Content-Based’ means that the search will analyze the actual contents of the image. The existing system does not evaluate the results upon attacks but in proposed system the results are also being evaluated on adding noise to the images and blurring the images. The overall average accuracy of the proposed system is 96% whereas that of existing system is 85%. Performance of the existing systems is checked on the maximum of 1000 images whereas the performance of the proposed system is checked on more than 5000 images.

scholarly journals Knn And Steerable Pyramid Based Enhanced Content Based Image Retrieval Mechanism

2018 ◽  
Vol 17 (2) ◽  
pp. 7215-7225
Author(s):  
Bohar Singh ◽  
Mrs. Mehak Aggarwal
Keyword(s):  
Image Retrieval ◽  
Image Data ◽  
Texture Features ◽  
Content Management ◽  
Content Based Image Retrieval ◽  
Visual Content ◽  
Image Region ◽  
Query Image ◽  
Image Retrieval System ◽  
Steerable Pyramid

Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. There has been an increase in attention towards the automated management and retrieval of digital images owing to the drastic development in the number and size of image databases. A significant and increasingly popular approach that aids in the retrieval of image data from a huge collection is called Content-based image retrieval (CBIR). Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this thesis work, we present a steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image region. To speed up retrieval and similarity computation, the database images are classified and the extracted regions are clustered according to their feature vectors using KNN algorithm We have used steerable pyramid to extract texture features from query image and classified database images and store them in feature features. 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.  Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time.

scholarly journals CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 294-299
Author(s):  
HARSHADA ANAND KHUTWAD ◽  
RAVINDRA JINADATTA VAIDYA
Keyword(s):  
Image Retrieval ◽  
Wavelet Decomposition ◽  
Texture Features ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Query Image ◽  
Shape Information ◽  
Color Correlogram ◽  
Similar Images ◽  
The Given

Content Based Image Retrieval is an interesting and most emerging field in the area of ‘Image Search’, finding similar images for the given query image from the image database. Current approaches include the use of color, texture and shape information. Considering these features in individual, most of the retrievals are poor in results and sometimes we are getting some non relevant images for the given query image. So, this dissertation proposes a method in which combination of color and texture features of the image is used to improve the retrieval results in terms of its accuracy. For color, color histogram based color correlogram technique and for texture wavelet decomposition technique is used. Color and texture based image

scholarly journals A State of Art on Content Based Image Retrieval Systems

2019 ◽  
Vol 8 (2S4) ◽  
pp. 297-301
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Data ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Query Image ◽  
Indexing Methods ◽  
Retrieval Systems ◽  
State Of Art

The digital image data is quick expanding in capacity and heterogeneity. The customary information retrieval approaches are cannot fulfill the client's need, so there isneed to present a proficient framework for Content Based Image Retrieval(CBIR). The CBIR is an appealing wellspring of precise and quick retrieval. CBIR goes for discovering imagedatabases for explicit images that are like a given query image dependent on its features.In this paper the methodology of content based image retrieval are examined, investigated and thought about. Here, the different image substance, for example, colour, texture and shape features are mined by utilizing differentfeature extraction procedures, and furthermore extraordinary distance measures, Relevance Feedback (RF) and indexing methods are used to improve the execution of the CBIR system.The existing exploration strategies are talked about with their benefits and negative marks, so the further research works can be focused more.

scholarly journals Capturing User Intention to Enhance Image Retrieval and Re-Ranking Efficiency Using One Click Image Search

2020 ◽  
Author(s):  
Dange B J ◽  
Yadav S K ◽  
Kshirsagar D B
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Search Efficiency ◽  
Image Search ◽  
Query Image ◽  
User Intention ◽  
Fusion Technique ◽  
Heterogeneous Features ◽  
Retrieval Efficiency ◽  
Similar Images

A Novel data fusion technique to support text-based and content-based image retrieval combining different heterogeneous features is proposed. The user need to give just a single click on an query image and images recovered by content based search are re-positioned dependent on their visual and texture similitudes to the query image.Textual and visual expansions are integrated to capture user intention without additional human feedback. Expanded keywords helps in extending positive model images and furthermore develop the image pool to include more relevant images. A lot of visual features which are both efficient and effective for image search are chosen. The n-dimensional feature vector for both colour and texture is reduced to single dimension each, used for comparing the similarity with query image using suitable distance metrics. Further only the images retrieved as a result of text based search and image re-ranking process are compared during run time for finding the similar images; not the entire database. This considerably reduces the computational complexity and improves the search efficiency. With improved feature extraction capturing textual and visual similarities, the proposed one click image search framework gives a productive robotized recovery of comparable images giving promising results with improvement in retrieval efficiency.

scholarly journals CONTENT BASED IMAGE RETRIEVAL USING MULTI-SVM TECHNIQUE FROM NOISY AND NON NOISY IMAGES

2020 ◽  
Vol 9 (8) ◽  
pp. 93-96
Keyword(s):  
Image Retrieval ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Mining Machine ◽  
Image Mining ◽  
Query Image ◽  
Processing Data ◽  
Average Accuracy ◽  
Information Image ◽  
New System

Image mining is a technique which handles the mining of information, image data association, or additional patterns not unambiguously stored in the images. It exploits methods from computer vision, image retrieval, image processing, data mining, machine learning, database, and artificial intelligence. In the proposed work, we have developed a new system that can retrieve the images from a dataset on the basis of contents of the query image. Here, ‘Content-Based’ means that the search will analyze the actual contents of the image. The existing system does not evaluate the results upon attacks but in proposed system the results are also being evaluated on adding noise to the images and blurring the images. The overall average accuracy of the proposed system is 96% whereas that of existing system is 85%. Performance of the existing systems is checked on the maximum of 1000 images whereas the performance of the proposed system is checked on more than 5000 images.

scholarly journals Image Retrieval System using Residual Neural Network in a Distributed Environment

2020 ◽  
Vol 8 (6) ◽  
pp. 4597-4605
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Retrieval System ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Features Selection ◽  
Query Image ◽  
Server Side ◽  
Image Retrieval System ◽  
Client Side

Development of Content-Based Image Retrieval systems supports retrieval of similar images based on selected features. Selection of appropriate features for this process is a difficult task. In this regard, deep learning concept helps in choosing appropriate features for retrieval. In this work, Content-Based Image Retrieval system is proposed using Convolution Neural Network known as Residual Neural Network model. The dataset used to build retrieval system is collection of web images 50,000 of 250 categories. The model is trained on 40% of image data and tested on 60% of data. When user submits a query image from the client-side, similar features are extracted by the model on server-side. Later, the features of query image are compared with trained images data and similarity is measured using the metric of Euclidean distance. The retrieved resultant images are displayed on Graphical User Interface. The results are comparatively higher with the existing systems. The proposed work is also compared with Google’s Image retrieval system for random query images and our proposed work has shown a better performance by 14.27%.

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