scholarly journals A New Content Based Search Mechanism for Image Retrieval Search Engine

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Image Retrieval ◽  
Search Engine ◽  
Data Storage ◽  
Search Engines ◽  
Input Image ◽  
Medical Field ◽  
Query Image ◽  
Search Mechanism ◽  
User Expectations ◽  
Diagnostics System

In the growing world of technology, where everything is available in just one click, the user expectations has increased with time. In the era of Search Engines, where Google, Yahoo are providing the facility to search through text and voice and image , it has become a complex work to handle all the operations and lot more of data storage is needed. It is also a time consuming process. In the proposed Image retrieval Search Engine, the user enters the queried image and that image is being matched with the template images . The proposed approach takes the input image with 15% accuracy to 100% accuracy to retrieve the intended image by the user. But it is found that due to the efficiency of the applied algorithm, in all cases, the retrieved images are with the same accuracy irrespective of the input query image accuracy. This implementation is very much useful in the fields of forensic, defense and diagnostics system in medical field etc. .

Retrieval effectiveness of image search engines

2019 ◽  
Vol 37 (1) ◽  
pp. 173-184 ◽  
Author(s):  
Aabid Hussain ◽  
Sumeer Gul ◽  
Tariq Ahmad Shah ◽  
Sheikh Shueb
Keyword(s):  
Image Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Keyword Search ◽  
Image Search ◽  
Image Size ◽  
Test Bed ◽  
Content Type ◽  
Retrieval Effectiveness ◽  
Search Feature

Purpose The purpose of this study is to explore the retrieval effectiveness of three image search engines (ISE) – Google Images, Yahoo Image Search and Picsearch in terms of their image retrieval capability. It is an effort to carry out a Cranfield experiment to know how efficient the commercial giants in the image search are and how efficient an image specific search engine is. Design/methodology/approach The keyword search feature of three ISEs – Google images, Yahoo Image Search and Picsearch – was exploited to make search with keyword captions of photos as query terms. Selected top ten images were used to act as a testbed for the study, as images were searched in accordance with features of the test bed. Features to be looked for included size (1200 × 800), format of images (JPEG/JPG) and the rank of the original image retrieved by ISEs under study. To gauge the overall retrieval effectiveness in terms of set standards, only first 50 result hits were checked. Retrieval efficiency of select ISEs were examined with respect to their precision and relative recall. Findings Yahoo Image Search outscores Google Images and Picsearch both in terms of precision and relative recall. Regarding other criteria – image size, image format and image rank in search results, Google Images is ahead of others. Research limitations/implications The study only takes into consideration basic image search feature, i.e. text-based search. Practical implications The study implies that image search engines should focus on relevant descriptions. The study evaluated text-based image retrieval facilities and thereby offers a choice to users to select best among the available ISEs for their use. Originality/value The study provides an insight into the effectiveness of the three ISEs. The study is one of the few studies to gauge retrieval effectiveness of ISEs. Study also produced key findings that are important for all ISE users and researchers and the Web image search industry. Findings of the study will also prove useful for search engine companies to improve their services.

scholarly journals An Novel Framework For Content Based Image Retrieval With Quality Assessment System using Optimal Deep Convolution Neural Network

2019 ◽  
Vol 8 (12) ◽  
pp. 3916-3926
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Vector ◽  
Structural Similarity ◽  
Extraction Process ◽  
Input Image ◽  
Assessment System ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Occurrence Matrix

Content based image retrieval (CBIR) models become popular for retrieving images connected to the query image (QI) from massive dataset. Feature extraction process in CBIR plays a vital role as it affects the system’s performance. This paper is focused on the design of deep learning (DL) model for feature extraction based CBIR model. The presented model utilizes a ResNet50 with co-occurrence matrix (RCM) model for CBIR. Here, the ResNet50 model is applied for feature extraction of the QI. Then, the extracted features are placed in the feature repository as a feature vector. The RCM model computes the feature vector for every input image and compares it with the features present in the repository. Then, the images with maximum resemblance will be retrieved from the dataset. In addition, the resemblance between the feature vectors is determined by the use of co-occurrence matrix subtraction process. Besides, structural similarity (SSIM) measure is applied for the validation of the similarity among the images. A comprehensive results analysis takes place by the use of Corel 10K dataset. The experimental outcome indicated the superiority of the RCM model with respect to precision, recall and SSIM.

The Perspectives of Improving Web Search Engine Quality

2011 ◽  
pp. 481-490 ◽  
Author(s):  
Jengchung V. Chen ◽  
Wen-Hsiang Lu ◽  
Kuan-Yu He ◽  
Yao-Sheng Chang
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Information Overload ◽  
Past Research ◽  
Quality Improvements ◽  
Syntactic Structures ◽  
Current Trends ◽  
Search Mechanism ◽  
Search Quality

With the fast growth of the Web, users often suffer from the problem of information overload, since many existing search engines respond to queries with many nonrelevant documents containing query terms based on the conventional search mechanism of keyword matching. In fact, both users and search engine developers had anticipated that this mechanism would reduce information overload by understanding user goals clearly. In this chapter, we will introduce some past research in Web search, and current trends focusing on how to improve the search quality in different perspectives of “what”, “how”, “where”, “when”, and “why”. Additionally, we will also briefly introduce some effective search quality improvements using link-structure-based search algorithms, such as PageRank and HITS. At the end of this chapter, we will introduce the idea of our proposed approach to improving search quality, which employs syntactic structures (verb-object pairs) to automatically identify potential user goals from search-result snippets. We also believe that understanding user goals more clearly and reducing information overload will become one of the major developments in commercial search engines in the future, since the amounts of information and resources continue to increase rapidly, and user needs will become more and more diverse.

scholarly journals Improved Content Based Medical Image Retrieval using PCA with SURF Features

2019 ◽  
Vol 8 (10S) ◽  
pp. 112-114
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Feature Vector ◽  
Principal Component ◽  
Medical Field ◽  
Huge Number ◽  
Query Image ◽  
Medical Image Retrieval ◽  
Image Retrieval System ◽  
Surf Features

In the computer era, the Content Based Image Retrieval system (CBIR) has most widely used in medical field and crime invention. During the last decade, CBIR emerged as powerful tool to efficiently retrieved images visually similar to query image. The basic process behind this concept is representation of image as feature vector and to measure the similarities between the images with distance between their corresponding feature vectors according to some metrics. The finding of correct features to represent images with, as well as the similarity metric that groups visually similar image together, are important milestone in construction of any CBIR system .The work in this paper focused on retrieve the correct query image from a huge number of medical image databases with the help of Principal Component Analysis (PCA) through SURF feature vector detection. The combination of this method produces an accurate and quick response than other conventional methods like SIFT and SURF feature vector based medical image retrieval.

scholarly journals CBIR SYSTEM USING COLOR HISTOGRAM AND WAVELET TRANSFORM FOR BLOOD CELLS IMAGES

2012 ◽  
pp. 8-11
Author(s):  
C. Rubina ◽  
S. Dasu
Keyword(s):  
Image Retrieval ◽  
Blood Cells ◽  
Color Histogram ◽  
Content Based Image Retrieval ◽  
Prototype System ◽  
Medical Field ◽  
Query Image ◽  
Cell Image ◽  
Current Content ◽  
Research Goal

The research in Content-based image retrieval is developing rapidly. It benefits many other fields, in particular the medical field as the need of having a better way of managing andretrieving digital images has increased.The aim of the thesis is to investigate performance of descriptors of blood cell image retrieval. In this process traditional wavelet based and global color histogram is investigated. The prototype system allows user to search by providing a query image and selecting one of four implemented methods. Research goal is enhancing current content-based image retrieval techniques. Results were obtained by experimenting to this proposed method is able to perform clinically relevant queries on image databases without user supervision.

scholarly journals Framework for Color and Texture Feature Fusion in Content Based Image Retrieval using Block Truncation Coding with Color Spaces

2020 ◽  
Vol 9 (3) ◽  
pp. 769-774
Keyword(s):  
Image Retrieval ◽  
Feature Fusion ◽  
Texture Feature ◽  
Local Binary Patterns ◽  
Input Image ◽  
Content Based Image Retrieval ◽  
Color Spaces ◽  
Query Image ◽  
Block Truncation Coding ◽  
Rate Of Increase

With tremendous growth in social media and digital technologies, generation, storing and transfer of huge amount of information over the internet is on the rise. Images or visual mode of communication have been prevailing and widely accepted as a mode of communication since ages. And with the growth of internet, the rate at which images are generated is growing exponentially. But the methods used to retrieve images are still very slow and inefficient, compared to the rate of increase in image databases. To cope up with this explosive increase in images, this information age has seen huge research advancement in Content Based Image Retrieval (CBIR). CBIR systems provide a way of utilizing the 3 major ways in which content is portrayed in images, those are shape, texture and color. In CBIR system, features are extracted from query image and similarity is found with features stored in database for retrieval. This provides an objective way of image retrieval, which is more efficient compared to subjective human annotation. Application specific CBIR systems have been developed and perform really well, but Generic CBIR systems are still under developed. Block Truncation Coding (BTC) has been chosen as a feature extractor. BTC applied directly on input image provides color content-based features of image and BTC applied after applying LBP on the image provide texture content-based features of image. Previous work consists of either color, shape or texture, but usage of more than one descriptor is still in research and might give better performance. The paper presents framework for color and texture feature fusion in content-based image retrieval using block truncation coding with color spaces. Experimentation is carried out on Wang Dataset of 1000 images consisting of 10 classes. Each class has 100 images in it. Obtained results have shown performance improvement using fusion of BTC extracted color features and texture features extracted with BTC applied on Local Binary Patterns (LBP). Conversion of color space from RGB to LUV is done using Kekre's LUV.

Performance evaluation of web search engines in image retrieval: An experimental study

2021 ◽  
pp. 026666692110102
Author(s):  
Mehrdad (Mozaffar) CheshmehSohrabi ◽  
Elham Adnani Sadati
Keyword(s):  
Experimental Study ◽  
Performance Evaluation ◽  
Image Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Image Search ◽  
Intense Competition ◽  
Significant Difference ◽  
General Search

This experimental study used a checklist to evaluate the performance of seven search engines consisting of four Image General Search Engines (IGSEs) (namely, Google, Yahoo DuckDuckGo and Bing), and three Image Specialized Search Engines (ISSEs) (namely, Flicker, PicSearch, and GettyImages) in image retrieval. The findings indicated that the recall average of Image General Search Engines and Image Specialized Search Engines was found to be 76.32% and 24/51% with the precision average of 82/08% and 32/21%, respectively. As the results showed, Yahoo, Google and DuckDuckGo ranked at the top in image retrieval with no significant difference. However, a remarkable superiority with almost 50% difference was observed between the general and specialized image search engines. It was also found that an intense competition existed between Google, Yahoo and DuckDuckGo in image retrieval. The overall results can provide valuable insights for new search engine designers and users in choosing the appropriate search engines for image retrieval. Moreover, the results obtained through the applied equations could be used in assessing and evaluating other search tools, including search engines.

Using Global Shape Descriptors for Content Medical-Based Image Retrieval

2013 ◽  
pp. 492-502
Author(s):  
Saïd Mahmoudi ◽  
Mohammed Benjelloun
Keyword(s):  
Fourier Transform ◽  
Image Retrieval ◽  
Real Time ◽  
Search Engine ◽  
Shape Descriptors ◽  
Query Image ◽  
Global Shape ◽  
Aided Diagnosis ◽  
Preliminary Classification

In this chapter, the authors propose a new method belonging to content medical-based image retrieval approaches and that uses a set of region-based shape descriptors. The search engine discussed in this work allows the classification of newly acquired medical images into some well known categories and also to get the images that are more similar to a query image. The final goal is to help the medical staff to annotate these images. To achieve this task, the authors propose a set of three descriptors that are based on: (1) Hu, (2) Zernike moments, and (3) Fourier transform-based signature, which are considered as region descriptors. The advantage of using this kind of global descriptor is that they are very fast, real time, and they do not need any segmentation step. The authors propose also a comparative study between these three approaches. The search engines are tested by using a database composed of 75 images that have different sizes, and that are classified into five classes. The results provided by the proposed retrieval approaches are given with high precision. The comparison between the three approaches is achieved using classification matrices and the recall/precision curves. The three proposed retrieval approaches produce accurate results in real time. This proves the advantage of using global shape features as a preliminary classification step in an automated aided diagnosis system.

Design and Implementation of Content-Based Natural Image Retrieval Approach Using Feature Distance

2020 ◽  
Vol 20 (02) ◽  
pp. 2050014
Author(s):  
S. L. Arunlal ◽  
N. Santhi ◽  
K. Ramar
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Back Propagation ◽  
Texture Feature ◽  
Input Image ◽  
Geographical Information ◽  
Natural Image ◽  
Medical Database ◽  
Query Image ◽  
Artificial Neural Network Ann

Generally, the database is a gathering of data that is arranged for simple storage, retrieval and modernize. This data comprises of numerous structures like text, table, and image, outline and chart and so on. Content-based image retrieval (CBIR) is valuable for calculating the huge amount of image databases and records and for distinguishes retrieving similar images. Rather than text-based searching, CBIR effectively recovers images that are similar like query image. CBIR assumes a significant role in various areas including restorative finding, industry estimation, geographical information satellite frameworks (GIS frameworks), and biometrics; online searching and authentic research, etc. Here different medical database images are considered to the CBIR procedure is done by the proposed strategy. The proposed method considers the input features are shape, texture feature, wavelet feature, and SIFT feature. To retrieve the input image based on the features, the suggested method utilizes artificial neural network (ANN) structure. Back-propagation technique, which is an organizational structure for learning is utilized for training the neural network framework. Trial demonstrates that the proposed work improves the results of the retrieval system. From the outcomes minimizes the image retrieval time and maximum Precision 87.3% in distance based ANN process.

scholarly journals CBIR with Partial Input of Unshaped Images Using Compressed-Pixel Matching Algorithm

2018 ◽  
Vol 7 (3.27) ◽  
pp. 206
Author(s):  
Ahilandeswari Thangarajan ◽  
Vivekanandan Kalimuthu
Keyword(s):  
Image Retrieval ◽  
Input Image ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Matching Algorithm ◽  
Pixel Matching

Many works have been done to find out whether given image is in the database using Content Based Image Retrieval (CBIR) techniques. However if the query image is unshaped or noise filled then retrieval of that image in the database is difficult .We propose an approach by which for any shape of input image the databases is searched and the most relevant image is retrieved. Results provides better accuracy than existing one and time elapsed also reduced because of making comparison after compression of both partial image and images from the database. The attainment of the proposed system is assessed using LFW and WANG image sets consisting of 2000 and 9990 images, respectively, and it measured with familiar methods with regard to precision and recall which demonstrates the advantages of the proposed approach. 

Sign in / Sign up

Export Citation Format

Share Document