scholarly journals Comparing the impact of Clustering with Content Based Image Retrieval Approaches for Plant Identification

2013 ◽  
Vol 9 (1) ◽  
pp. 985-994
Author(s):  
Komal Asrani ◽  
Renu Jain
Keyword(s):  
Image Retrieval ◽  
Processing Time ◽  
Search Time ◽  
Human Perception ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Plant Identification ◽  
Retrieval Time ◽  
The Impact ◽  
Leaf Image

Contour Based retrieval of images is an active and challenging field of research.  Among various parameters available for contour based image retrieval, shape is considered an important aspect because it is closest to the human perception. Most of the shape based image retrieval methods require large processing time for generating accurate results due to huge database. To reduce the search time, we have divided the database into clusters on the basis of eccentricity of leaf using K-Means approach. After making the clusters, different contour based approaches are applied for leaf/plant identification and results are compared.  The leaf image is processed to generate feature vectors which are stored in database.  We have used Swedish leaf image database (SLID) consisting of 15 species with 75 leaves per class and total of 1125 leaf images. In this paper, we compare results of contour based retrieval approaches with and without clustering. From these results, it is found that by incorporating clustering, performance of contour based retrieval approaches remains same but retrieval time is reduced.

scholarly journals Partitioning Medical Image Databases for Content-based Queries on a Grid

2005 ◽  
Vol 44 (02) ◽  
pp. 154-160 ◽  
Author(s):  
V. Breton ◽  
I. E. Magnin ◽  
J. Montagnat
Keyword(s):  
Theoretical Model ◽  
Image Retrieval ◽  
Medical Image ◽  
Computation Time ◽  
Image Database ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Medical Databases ◽  
Grid Nodes ◽  
The Impact

Summary Objectives: In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. Methods: A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. Results: We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Conclusions: Grids are promising for content-based image retrieval in medical databases.

scholarly journals An Efficient Medical Content Based Image Retrieval with Lenient Relevance Feedback

2018 ◽  
Vol 7 (3.6) ◽  
pp. 175 ◽  
Author(s):  
R I. Heaven Rose ◽  
A C. Subajini
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Early Stage ◽  
Human Perception ◽  
Feedback System ◽  
Content Based Image Retrieval ◽  
Research Issues ◽  
Retrieval Time ◽  
System User ◽  
High Level

Content Based Image Retrieval (CBIR) for medical imageries is still in its early stage.  There are many challenging research issues.  Retrieve similar images only is the current problem in medical CBIR. One idea to solve this difficult is minimizing the gap among two descriptions i.e. low level extracted features of image and high level human perception of image.  There are various Relevance Feedback (WF) methods have been considered to minimize the semantic gap in medical CBIR system. But most of them were deals with hard Feedback. In Hard Feedback system user can interact with the system in one query session. We recommend to aid the usage of lenient relevance response to better capture the intention of users. The meta-knowledge mined from multiple user’s experience be able to   increase the precision of subsequent image recovery results. Here we suggest an algorithm to mine lenient association rules from the group of suggestion i.e. image weight value given by the user. To reduce the amount of strong rules we offer two rule lessening techniques related to redundancy detection and confidence quantization.  Best first search and Binary search methods are similarly applied to advance the procedure of weight interface. The effectiveness of the offered system is assessed regarding precision and average retrieval time. The experimental results on medical images display that the proposed method is able to improve the accuracy of medical CBIR system and reduces the retrieval time than other usual methods.  

A Perceptual Approach for Image Representation and Retrieval

2011 ◽  
pp. 121-141
Author(s):  
Noureddine Abbadeni
Keyword(s):  
Image Retrieval ◽  
Image Representation ◽  
Rank Correlation ◽  
Human Perception ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Data Set ◽  
Psychometric Method ◽  
Results Merging ◽  
Spearman Rank Correlation Coefficient

This chapter describes an approach based on human perception to content-based image representation and retrieval. We consider textured images and propose to model the textural content of images by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast and busyness. The proposed computational measures are based on two representations: the original images representation and the autocovariance function (associated with images) representation. The correspondence of the proposed computational measures to human judgments is shown using a psychometric method based on the Spearman rank-correlation coefficient. The set of computational measures is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results show a strong correlation between the proposed computational textural measures and human perceptual judgments. The benchmarking of retrieval performance, done using the recall measure, shows interesting results. Furthermore, results merging/fusion returned by each of the two representations is shown to allow significant improvement in retrieval effectiveness.

A NOVEL INDEXING AND ACCESS MECHANISM USING AFFINITY HYBRID TREE FOR CONTENT-BASED IMAGE RETRIEVAL IN MULTIMEDIA DATABASES

2007 ◽  
Vol 01 (02) ◽  
pp. 147-170 ◽  
Author(s):  
KASTURI CHATTERJEE ◽  
SHU-CHING CHEN
Keyword(s):  
Image Retrieval ◽  
Metric Spaces ◽  
Nearest Neighbor ◽  
Human Perception ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Index Structure ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Computational Overhead

An efficient access and indexing framework, called Affinity Hybrid Tree (AH-Tree), is proposed which combines feature and metric spaces in a novel way. The proposed framework helps to organize large image databases and support popular multimedia retrieval mechanisms like Content-Based Image Retrieval (CBIR). It is efficient in terms of computational overhead and fairly accurate in producing query results close to human perception. AH-Tree, by being able to introduce the high level semantic image relationship as it is in its index structure, solves the problem of translating the content-similarity measurement into feature level equivalence which is both painstaking and error-prone. Algorithms for similarity (range and k-nearest neighbor) queries are implemented and extensive experiments are performed which produces encouraging results with low I/O and distance computations and high precision of query results.

scholarly journals Interactive Content Based Image Retrieval using Multiuser Feedback

2017 ◽  
Vol 1 (4) ◽  
pp. 165
Author(s):  
M. Premkumar ◽  
R. Sowmya
Keyword(s):  
Image Retrieval ◽  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Human Perception ◽  
Content Based Image Retrieval ◽  
Rule Mining ◽  
Query Image ◽  
Feedback Techniques

Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

Fast Image Retrieval Prototype using Color Descriptor

2016 ◽  
Vol 1 (22) ◽  
pp. 745-758
Author(s):  
Bushra Abdul-Kareem Abdul-Azeez
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Image Database ◽  
Query Image ◽  
Color Feature ◽  
Retrieval Time ◽  
Dominant Color ◽  
Color Descriptor

In recent years, image retrieval prototypes become important and increased noticeably. Color feature is one of the most significant features to represent image. In this paper, we use a Dominant Color (DC) feature to represent images where each image represented by 8-DCs as maximum. Based on DCs values, image database is indexed using 3-D RGB partitioning color space. This is to reduce searching process where once a query image is given to the prototype; it will not search the whole database. Proposed technique will identify the partition and search the image within this partition only. According to the proposed method, extensive experiments were conducted on Corel databases. As a result, the retrieval time is reduced significantly without degradation to precision of retrieval.

COMPARATIVE ANALYSIS OF IMAGE RETRIEVAL TECHNIQUES IN CYBERSPACE

2020 ◽  
Vol 8 (1) ◽  
pp. 01-10
Author(s):  
Rajeev Gupta ◽  
Virender Singh
Keyword(s):  
Comparative Analysis ◽  
Image Retrieval ◽  
Search Engines ◽  
Web Search ◽  
Content Based Image Retrieval ◽  
Retrieval Time ◽  
User Query ◽  
Web Search Engines ◽  
Research Challenge ◽  
The Web

Purpose: With the popularity and remarkable usage of digital images in various domains, the existing image retrieval techniques need to be enhanced. The content-based image retrieval is playing a vital role to retrieve the requested data from the database available in cyberspace. CBIR from cyberspace is a popular and interesting research area nowadays for a better outcome. The searching and downloading of the requested images accurately based on meta-data from the cyberspace by using CBIR techniques is a challenging task. The purpose of this study is to explore the various image retrieval techniques for retrieving the data available in cyberspace.  Methodology: Whenever a user wishes to retrieve an image from the web, using present search engines, a bunch of images is retrieved based on a user query. But, most of the resultant images are unrelated to the user query. Here, the user puts their text-based query in the web-based search engine and compute the related images and retrieval time. Main Findings:  This study compares the accuracy and retrieval-time of the requested image. After the detailed analysis, the main finding is none of the used web-search engines viz. Flickr, Pixabay, Shutterstock, Bing, Everypixel, retrieved the accurate related images based on the entered query.   Implications: This study is discussing and performs a comparative analysis of various content-based image retrieval techniques from cyberspace. Novelty of Study: Research community has been making efforts towards efficient retrieval of useful images from the web but this problem has not been solved and it still prevails as an open research challenge. This study makes some efforts to resolve this research challenge and perform a comparative analysis of the outcome of various web-search engines.

Sign in / Sign up

Export Citation Format

Share Document