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.

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

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

Machine-Learning-Based Image Feature Selection

2018 ◽  
pp. 65-73
Author(s):  
Vivek K. Verma ◽  
Tarun Jain
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Big Data ◽  
Image Retrieval ◽  
Search Algorithms ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Large Databases ◽  
Retrieval Problem

This is the age of big data where aggregating information is simple and keeping it economical. Tragically, as the measure of machine intelligible data builds, the capacity to comprehend and make utilization of it doesn't keep pace with its development. In content-based image retrieval (CBIR) applications, every database needs its comparing parameter setting for feature extraction. CBIR is the application of computer vision techniques to the image retrieval problem that is the problem of searching for digital images in large databases. In any case, the vast majority of the CBIR frameworks perform ordering by an arrangement of settled and pre-particular parameters. All the major machine-learning-based search algorithms have discussed in this chapter for better understanding related with the image retrieval accuracy. The efficiency of FS using machine learning compared with some other search algorithms and observed for the improvement of the CBIR system.

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.

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