Content-Based Image Retrieval Using Shape Features

2018 ◽  
pp. 39-61
Keyword(s):  
Image Retrieval ◽  
Data Base ◽  
Feature Vector ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Geometric Features ◽  
Shape Features ◽  
Base Object

This chapter presents CBIR methodologies for extracting geometric and margin features of objects in images and constructed as feature vector. This approach is unique in nature as the size of the feature is relatively small and capable of discriminating the query object with the data base object. These geometric features measure the object characteristics in terms of its shape and margin. Manhattan distance is used for measuring the similarity between query images and the database images for retrieving relevant images from the database.

Evaluation of Content Based Image Retrieval Technique Using an Efficient Hybrid Model

2020 ◽  
Vol 17 (4) ◽  
pp. 1885-1888
Author(s):  
M. A. Muthiah ◽  
N. Mathan ◽  
E. Logashanmugam
Keyword(s):  
Image Retrieval ◽  
Hybrid Model ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Shape Features ◽  
Query Image ◽  
Visual Technology ◽  
Retrieval Technique ◽  
Future Work ◽  
Calculated Distance

Due to vast enhancement in the field of visual technology, there are various sets of images. In order to reduce the complexity in retrieval of relevant images CBIR (Content Based Image Retrieval) technique can be used. CBIR using only color feature does not result in required output. So in this paper we introduced the concept of hybrid model which deals with color, texture along with shape features which gives an efficient output. A set of images are used to test the accuracy and the precision of each methods. Using Euclidean distance and Manhattan distance, similarity between query image and all the other images in database are calculated. Then the calculated distance values are arranged in ascending order. Based on this required images are retrieved. Experiment results shows that Hybrid model method had high accuracy and precise output compared to Color Histogram. Future work will be made to add one more feature (shape features) in order to get better results.

scholarly journals CBIR Using Slant Transform Using DC & AC Coefficients

2018 ◽  
Vol 7 (3.6) ◽  
pp. 276 ◽  
Author(s):  
N Sravani ◽  
K Veera Swamy
Keyword(s):  
Data Base ◽  
Feature Vector ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Vector Data ◽  
Query Image ◽  
Retrieval Technique ◽  
Slant Transform ◽  
Measuring Techniques ◽  
The Difference

In the CBIR- (Content Based Image Retrieval) technique requires low-level or primitive features- color, texture, or  other data that can be taken from its image Extracting feature vectors of database images as well as query image can be calculated with the help of slant transform by considering DC & 3 AC coefficients obtained in each block of an image. Slant transform represents the gradual brightness changes in an image line effectively. By calculating the difference between feature vector data base and feature vector for a query by using the distance measuring techniques. The vector of the smaller distance is the closest to query image. The experiment is performed in the Corel 500 Image Database. Finally, CBIR results are evaluated by the recall, precision, and F-Score.  

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.

Comparison of Color, Texture and ICM Features in CBIR System

2011 ◽  
Vol 403-408 ◽  
pp. 13-19 ◽  
Author(s):  
Sonali Bhadoria ◽  
Meenakshi Madugunki ◽  
C.G. Dethe ◽  
Preeti Aggarwal
Keyword(s):  
Computer Vision ◽  
Image Retrieval ◽  
Data Base ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Retrieval Process ◽  
Retrieval Performance ◽  
Research Areas ◽  
Matching Techniques ◽  
Index Search

Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. There are various features which can be extracted from the image which gives different performance in retrieving the image.al systems. In this paper we have tried to compare the effect of using different features on the same data base to implement CBIR system. We have tried to analyse the retrieval performance for each feature. We have compared different features as well as the combinations of them to improve the performance. We have also compared the effect of different matching techniques on the retrieval process.

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