Quick interactive image search in huge databases using Content-Based image retrieval

2015 ◽  
Author(s):  
Sushant Shrikant Hiwale ◽  
Dhanraj Dhotre ◽  
G.R. Bamnote
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Image Search

Nested Partitions Properties for Spatial Content Image Retrieval

2012 ◽  
pp. 240-269
Author(s):  
Dmitry Kinoshenko ◽  
Vladimir Mashtalir ◽  
Vladislav Shlyakhov ◽  
Elena Yegorova
Keyword(s):  
Image Retrieval ◽  
Hierarchical Models ◽  
Search Algorithms ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Nested Partitions ◽  
Specific Search ◽  
Spatial Content

In this paper, a metric on partitions of arbitrary measurable sets and its special properties for metrical content-based image retrieval based on the ‘spatial’ semantic of images is proposed. This approach considers images represented in the form of nested partitions produced by any segmentations, which are used to express a degree of information refinement or roughening. In doing so, this not only corresponds to rational content control but also ensures creation of specific search algorithms (e.g., invariant to image background) and synthesizes hierarchical models of image search by reducing the number of query and database elements match operations.

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

Metrical Properties of Nested Partitions for Image Retrieval

2011 ◽  
pp. 18-49
Author(s):  
Dmitry Kinoshenko ◽  
Vladimir Mashtalir ◽  
Vladislav Shlyakhov ◽  
Elena Yegorova
Keyword(s):  
Image Retrieval ◽  
Hierarchical Models ◽  
Search Algorithms ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Nested Partitions ◽  
Specific Search

This chapter proposes a metric on partitions of arbitrary measurable sets and its special properties for metrical content-based image retrieval based on the ‘spatial’ semantic of images. The approach considers images represented in the form of nested partitions produced by any segmentations. Nested partitions representation expresses a degree of information refinement or roughening and so not only corresponds to rational content control but also ensures creation of specific search algorithms (e.g. invariant to image background) and synthesize hierarchical models of image search reducing the number of query and database elements match operations.

scholarly journals CONTENT-BASED IMAGE RETRIEVAL-SKETCH FOR MATCH

2013 ◽  
pp. 17-22
Author(s):  
P.HARSHA VARDHAN REDDY ◽  
K Saradha
Keyword(s):  
Image Retrieval ◽  
Digital Libraries ◽  
Color Image ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Visual Content ◽  
Research Areas ◽  
Full Color ◽  
Photo Sharing ◽  
Processing Steps

The content based image retrieval (CBIR) is one of the most popular, rising research areas of the digital image pro-cessing. Most of the available image search tools, such as Google Images and Yahoo! Image search, are based on textual annotation of images. In these tools images are manually annotated with keywords and then retrieved using text-based search methods. The performances of these systems are not satisfactory. The goal of CBIR is to extract visual content of an image automatically, like color, texture, or shape. This paper aims to introduce the problems and challenges concerned with the design and the creation of CBIR systems, which is based on a free hand sketch (Sketch based image retrieval – SBIR). The used descriptor is constructed after such special sequence of pre-processing steps that the transformed full color image and the sketch can be compared. We have studied EHD, HOG and SIFT. Experimental results on two sample databases showed good results. Overall, the results show that the sketch based system allows users an intuitive access to search-tools. The SBIR technology can be used in several applications such as digital libraries, crime prevention, photo sharing sites. Such a system has great value in apprehending suspects and identifying victims in forensics and law enforcement. A possible application is matching a forensic sketch to a gallery of mug shot images. The area of retrieve images based on the visual content of the query picture intense recently, which demands on the quite wide methodology spectrum on the area of the image processing.

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 Real-time Multi-object Face Recognition Using Content Based Image Retrieval (CBIR)

2018 ◽  
Vol 8 (5) ◽  
pp. 2812
Author(s):  
Muhammad Fachrurrozi ◽  
Saparudin Saparudin ◽  
Erwin Erwin ◽  
Mardiana Mardiana ◽  
Clara Fin Badillah ◽  
...  
Keyword(s):  
Face Recognition ◽  
Image Retrieval ◽  
Real Time ◽  
Euclidean Distance ◽  
Time Trial ◽  
Recognition System ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Face Recognition System

<p>Face recognition system in real time is divided into three processes, namely feature extraction, clustering, detection, and recognition. Each of these stages uses different methods, Local Binary Pattern (LBP), Agglomerative Hierarchical Clustering (AHC) and Euclidean Distance. Multi-face image search using Content Based Image Retrieval (CBIR) method. CBIR performs image search by image feature itself. Based on real time trial results, the accuracy value obtained is 61.64%. </p><p class="Abstract"> </p>

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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