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

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 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 A Review on LBP in Image Retrieval System for Future Enhancement and Vector Images

2020 ◽  
pp. 155-161
Author(s):  
K Rajalakshmi ◽  
V Krishna Dharshini ◽  
S Selva Meena
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Query Image ◽  
Matching Algorithm ◽  
Image Retrieval System ◽  
Similar Images ◽  
Vector Images ◽  
Similar Kind

Content-Based Image Retrieval is a process to retrieve the similar images from the large set of image database corresponding to the query image. In CBIR low level or pixel level features such as color, texture and shape of the images are extracted and on the basis of similarity matching algorithm the required similar kind of images are retrieved from the image database. To understand the evaluation and evolution of CBIR system various research was studied and various research is going on this way also. In this paper, we have discussed some of the popular pixel level feature extraction techniques for Content-Based Image Retrieval and we also present here about the performance of each technique.

Automatic Extrapolation of User Intention for Internet Image Search without Duplication

2014 ◽  
Vol 573 ◽  
pp. 447-452
Author(s):  
Shiney R. Addlin ◽  
Kumar T. Saravana ◽  
Mary M. Roslin
Keyword(s):  
Visual Information ◽  
Image Search ◽  
Query Image ◽  
User Intention ◽  
Search Results ◽  
Adaptive Weight ◽  
Similar Images ◽  
Text Features ◽  
Textual Content ◽  
The Given

.Image search engines (e.g. Google Image Search, Bing Image Search) mostly depends on the given query surrounding text features. It increases complexity to interpret users search intention only by giving single query keywords and this leads to ambiguous and noisy search results. To solve the ambiguity in the image search, consider visual information along with the text features. In this approach user has to click a single search return image and the search results are re-ranked based on the similarity in visual and textual content. Our work is to capture user search intention by doing one-click image search has four steps. Adaptive weight categories are predefined to category the query image and this helps to re-rank the text based search results. Keywords are expanded based on the selected query image visual content that helps to capture user intention. Based on the expanded keywords image pool get expanded that contain more relevant images with the query image. Expanded keywords are also used to expand the query image that lead to multiple positive similar images and the similarity metrics are learned for page re-ranking. Re-ranking of similarity images to the query image based on photo quality assessment to provide better search results.

scholarly journals Proposed a Content-Based Image Retrieval System Based on the Shape and Texture Features

2019 ◽  
Vol 8 (11) ◽  
pp. 2185-2192
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Visual Image ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Feature Extraction Method ◽  
Image Retrieval System ◽  
Reference Images ◽  
Similar Images

In this paper, we proposed a fusion feature extraction method for content based image retrieval. The feature is extracted by focusing on the texture and shape features of the visual image by using the Local Binary Pattern (LBP – texture feature) and Edge Histogram Descriptor (EHD – shape feature). The SVD is used for decreasing the number of the feature vector of images. The Kd-tree is used for reducing the retrieval time. The input to this system is a query image and Database (the reference images) and the output is the top n most similar images for the query image. The proposed system is evaluated by using (precision and recall) to measure the retrieval effectiveness. The values of the recall are between [43% –93%] and the average recall is 64.3%. The values of precision are between [30%-100%] and the average is 72.86% for the entire system and for both databases

scholarly journals Content based Image Retrieval using Statistical Parameters of a Medical Image

2019 ◽  
Vol 8 (3) ◽  
pp. 3649-3653
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Content Based Image Retrieval ◽  
Statistical Parameters ◽  
Query Image ◽  
Similar Images ◽  
Matching Score ◽  
The Cost ◽  
The Given

We present a framework that permits in classifying medical images so as to recognize conceivable diseases that affected. This is done by Image retrieval from the collection of dataset by inputting the query image. Content based Image retrieval (CBIR) is the way toward seeking comparable pictures from a picture database dependent on the visual substance of the given query image. Even though some studies present general method in image extraction, there are no efficient methods in medical image retrieval with accuracy. To overcome and to eliminate these flaws our proposed CBIR method examined with the accurate and efficient way for feature extraction from medical images. The images used are grey scale image. The dataset holds the n number of images related to medical particularly brain tumor images. To retrieve the related images from the dataset and get the corresponding details, image is given as an input i.e., query image. Initially, the query image is analyzed by shape, texture and histogram and the result obtained from this is compared with the similar images in dataset. The similarities between the images are found by implementing the Matching Score algorithm. This algorithm provides accuracy in matching the image that helps greatly at the time of classification. The results of computation is said to be the features for the given image. Also the cost for processing the image is comparatively low. The technique has been examined on standard image dataset and satisfactory results have been achieved

scholarly journals An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Shalaw Faraj Salih ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Retrieval ◽  
Communication Technologies ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Retrieval Technique ◽  
Current State ◽  
Similar Images ◽  
The Impact ◽  
Precision Rate ◽  
Color Texture

Applications for retrieving similar images from a large collection of images have increased significantly in various fields with the rapid advancement of digital communication technologies and exponential evolution in the usage of the Internet. Content-based image retrieval (CBIR) is a technique to find similar images on the basis of extracting the visual features such as color, texture, and/or shape from the images themselves. During the retrieval process, features and descriptors of the query image are compared to those of the images in the database to rank each indexed image accordingly to its distance to the query image. This paper has developed a new CBIR technique which entails two layers, called bi-layers. In the first layer, all images in the database are compared to the query image based on the bag of features (BoF) technique, and hence, the M most similar images to the query image are retrieved. In the second layer, the M images obtained from the first layer are compared to the query image based on the color, texture, and shape features to retrieve the N number of the most similar images to the query image. The proposed technique has been evaluated using a well-known dataset of images called Corel-1K. The obtained results revealed the impact of exploring the idea of bi-layers in improving the precision rate in comparison to the current state-of-the-art techniques in which achieved precision rate of 82.27% and 76.13% for top-10 and top-20, respectively.

scholarly journals An Appraisal of Content-Based Image Retrieval (CBIR) Methods

2019 ◽  
pp. 1-15
Author(s):  
J. O. Olaleke ◽  
A. O. Adetunmbi ◽  
B. A. Ojokoh ◽  
Iroju Olaronke
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Digital Libraries ◽  
Large Scale ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Advantages And Disadvantages ◽  
Feature Extraction And Selection ◽  
Similar Images ◽  
Google Search

Background: Content Based Image Retrieval (CBIR) is an aspect of computer vision and image processing that finds images that are similar to a given query image in a large scale database using the visual contents of images such as colour, texture, shape, and spatial arrangement of regions of interest (ROIs) rather than manually annotated textual keywords. A CBIR system represents an image as a feature vector and measures the similarity between the image and other images in the database for the purpose of retrieving similar images with minimal human intervention. The CBIR system has been deployed in several fields such as fingerprint identification, biodiversity information systems, digital libraries, Architectural and Engineering design, crime prevention, historical research and medicine. There are several steps involved in the development of CBIR systems. Typical examples of these steps include feature extraction and selection, indexing and similarity measurement. Problem: However, each of these steps has its own method. Nevertheless, there is no universally acceptable method for retrieving similar images in CBIR. Aim: Hence, this study examines the diverse methods used in CBIR systems. This is with the aim of revealing the strengths and weakness of each of these methods. Methodology: Literatures that are related to the subject matter were sought in three scientific electronic databases namely CiteseerX, Science Direct and Google scholar. The Google search engine was used to search for documents and WebPages that are appropriate to the study. Results: The result of the study revealed that three main features are usually extracted during CBIR. These features include colour, shape and text. The study also revealed that diverse methods that can be used for extracting each of the features in CBIR. For instance, colour space, colour histogram, colour moments, geometric moment as well as colour correlogram can be used for extracting colour features. The commonly used methods for texture feature extraction include statistical, model-based, and transform-based methods while the edge method, Fourier transform and Zernike methods can be used for extracting shape features. Contributions: The paper highlights the benefits and challenges of diverse methods used in CBIR. This is with the aim of revealing the methods that are more efficient for CBIR. Conclusion: Each of the CBIR methods has their own advantages and disadvantages. However, there is a need for a further work that will validate the reliability and efficiency of each of the method.

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%.

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