A Hierarchial Classification Technique for Semantics-Based Image Retrieval

2011 ◽  
pp. 311-333 ◽  
Author(s):  
Mohammed Lamine Kherfi ◽  
Djemel Ziou
Keyword(s):  
Image Retrieval ◽  
Retrieval Accuracy ◽  
The Third ◽  
Retrieval Systems ◽  
Probabilistic Classification ◽  
Semantic Concepts ◽  
Similar Images ◽  
Hierarchical Index ◽  
Upper Level ◽  
Abstraction Levels

We present a new approach for improving image retrieval accuracy by integrating semantic concepts. First, images are represented according to different abstraction levels. At the lowest level, they are represented with visual features. At the upper level, they are represented with a set of very specific keywords. At the subsequent levels, they are represented with more general keywords. Second, visual content together with keywords are used to create a hierarchical index. A probabilistic classification approach is proposed, which allows to group similar images into the same class. Finally this index is exploited to define three retrieval mechanisms: the first is text-based, the second is content-based, and the third is a combination of both. Experiments show that our combination allows to nicely narrow the semantic gap encountered by most current image retrieval systems. Furthermore, we show that the proposed method helps reducing retrieval time and improving retrieval accuracy.

An Image Clustering and Feedback-based Retrieval Framework

2010 ◽  
Vol 1 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Single Object ◽  
Image Region ◽  
Retrieval Accuracy ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

scholarly journals Retrieval of Images using Combination of Features as Color, Color Moments and Hu Moments

2019 ◽  
Vol 7 (5) ◽  
pp. 09-21
Author(s):  
RAJKUMAR RAJ ◽  
Dr. M V Sudhamani
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Retrieval Performance ◽  
Recall Accuracy ◽  
Hsv Color Space ◽  
Low Level ◽  
Digital Era ◽  
Retrieval Systems ◽  
Hu Moments ◽  
Similar Images

In today’s digital era, several of the image retrieval systems focus on retrieving images using features from images themselves such as color, shape and textures and are referred as low-level features. In this proposed work, the features like color with HSV color space, color moments and Hu moments are employed to retrieve similar images. Various experimentations were conducted on Wang’s database images to test the combination of features for higher performance using precision, recall, accuracy and f-score. The results obtained are compared with one another and also with existing works. The retrieval performance is found to be high for proposed system against existing works.

A NOVEL TWO-LEVEL COLOR IMAGE RETRIEVAL METHOD

2011 ◽  
Vol 11 (03) ◽  
pp. 339-353 ◽  
Author(s):  
XING-YUAN WANG ◽  
ZHI-FENG CHEN ◽  
JIAO-JIAO YUN
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Color Image ◽  
Image Databases ◽  
Color Model ◽  
Retrieval Method ◽  
Retrieval Accuracy ◽  
Similar Images

This paper presents an effective two-level color image retrieval method based on the RGB color model. For the purpose of effectively retrieving more similar images from the digital image databases, we divide the image into different regions and set bigger weight for the region we focus on. In addition, we set different weights for each RGB component of the color image according to the main hue of it. As a result, this scheme can enhance the retrieval accuracy that is measured in terms of the recall and precision.

scholarly journals Image retrieval techniques: a survey

2017 ◽  
Vol 7 (1.2) ◽  
pp. 215 ◽  
Author(s):  
Aman Dureja ◽  
Payal Pahwa
Keyword(s):  
Image Retrieval ◽  
Medical Systems ◽  
Digital Repositories ◽  
Advantages And Disadvantages ◽  
Retrieval Systems ◽  
Large Databases ◽  
Base Method ◽  
Efficient Retrieval ◽  
Similar Images ◽  
Large Image Databases

In the recent years, the development in computer technologies and multimedia applications has led to the production of huge digital images and large image databases, and it is increasing rapidly. There are several different areas in which image retrieval plays a crucial role like Medical systems, Forensic Labs, Tourism Promotion, etc. Thus retrieval of similar images is a challenge. To tackle this rapid growth in digital repositories it is necessary to develop image retrieval systems, which can operate on large databases. There are basically three techniques, which is useful for efficient retrieval of images. With these techniques, the number of methods has been modified for the efficient image retrieval of images. In this paper, we presented the survey of different techniques that has been used starting from Image retrieval using visual features and latest by the deep learning with CNN that contains the number of layers and now becomes the best base method for retrieval of images from the large databases. In the last section, we have made the analysis between various developed techniques and showed the advantages and disadvantages of various techniques.

Design Patent Retrieval Based on Gabor Wavelet and LBP

2015 ◽  
Vol 743 ◽  
pp. 503-509
Author(s):  
Q.Q. Li ◽  
C.S. Zhou ◽  
X.Q. Lv ◽  
H.Y. Yang ◽  
K. Zhang
Keyword(s):  
Image Retrieval ◽  
Gabor Filter ◽  
Texture Feature ◽  
Gabor Wavelet ◽  
Retrieval Method ◽  
Patent Retrieval ◽  
Retrieval Accuracy ◽  
Design Patent ◽  
Low Pass ◽  
Similar Images

Due to the diversity and complexity of design patent images, it is difficult to retrieve well if extracting features from images directly. A design patent image retrieval method based on Gabor filter and LBP is proposed in the paper. Firstly, doing low-pass filtering to the normalized images with Gabor filter to amplify the images’ details, then extracting image’s texture feature with LBP algorithm, calculating images’ similarity according to the distance formula after feature vectors’ internal normalization, finally return several similar images. The experimental results show that this retrieval method get better retrieval accuracy and correct rate.

Evaluation of Near-Duplicate Image Retrieval Algorithms for the Identification of Celebrities in Web Images

2013 ◽  
Vol 765-767 ◽  
pp. 1431-1435
Author(s):  
Feng Cai Qiao ◽  
Xin Zhang ◽  
Hui Wang ◽  
Jian Ping Cao
Keyword(s):  
Image Retrieval ◽  
Research Problem ◽  
Intermediate Step ◽  
Retrieval Accuracy ◽  
Similar Images ◽  
On Line ◽  
Feature Based ◽  
Web Images ◽  
Sift Features ◽  
Comprehensive Study

Near-duplicate image retrieval is a classical research problem in computer vision, for which a large number of diverse approaches have been proposed. Recent studies have revealed that it can be used as an intermediate step to implement search-based celebrity identification given the existence of huge volume of user-tagged or text-surrounded celebrity images on the web. However, the effectiveness of existing near-duplicate image retrieval methods for such a task still remains unclear. To address this issue, this paper presents a comprehensive study of the existing near-duplicate image retrieval methods in a structural way. Four representatives of the existing methods, i.e. hash signature, mean SSIM, BoVW with SIFT features and ARG, are experimentally evaluated using a self-constructed dataset containing 24762 images of 15 top searched celebrities collected using 6 news search engines and the Google image search engine. The experimental results reveal that, compared with global feature based methods, local feature based ones are usually more appropriate for the task of celebrity identification in web images, as they can deal with partial duplicate and scene similar images better. In particular, BoVW with SIFT features is recommended as it provides the best trade-off between on-line speed and retrieval accuracy.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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