scholarly journals A State of Art on Content Based Image Retrieval Systems

2019 ◽  
Vol 8 (2S4) ◽  
pp. 297-301
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Data ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Query Image ◽  
Indexing Methods ◽  
Retrieval Systems ◽  
State Of Art

The digital image data is quick expanding in capacity and heterogeneity. The customary information retrieval approaches are cannot fulfill the client's need, so there isneed to present a proficient framework for Content Based Image Retrieval(CBIR). The CBIR is an appealing wellspring of precise and quick retrieval. CBIR goes for discovering imagedatabases for explicit images that are like a given query image dependent on its features.In this paper the methodology of content based image retrieval are examined, investigated and thought about. Here, the different image substance, for example, colour, texture and shape features are mined by utilizing differentfeature extraction procedures, and furthermore extraordinary distance measures, Relevance Feedback (RF) and indexing methods are used to improve the execution of the CBIR system.The existing exploration strategies are talked about with their benefits and negative marks, so the further research works can be focused more.

scholarly journals Implementation of early and late fusion methods for content-based image retrieval

2021 ◽  
Vol 8 (7) ◽  
pp. 97-105
Author(s):  
Ali Ahmed ◽  
◽  
Sara Mohamed ◽  
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Late Fusion ◽  
Query Image ◽  
Fusion Methods ◽  
And Performance ◽  
Image Repository

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

scholarly journals A simple multi-feature based stereoscopic medical image retrieval system

2019 ◽  
Vol 25 (2) ◽  
pp. 127-130
Author(s):  
K.A. Shaheer Abubacker ◽  
J. Sutha ◽  
K.A. Shahul Hameed
Keyword(s):  
Image Retrieval ◽  
Distance Measure ◽  
Performance Outcomes ◽  
Stereo Image ◽  
Distance Measures ◽  
Absolute Difference ◽  
Disparity Map ◽  
Query Image ◽  
Retrieval Systems ◽  
Feature Based

Abstract This paper describes a method of retrieving stereoscopic medical images from the database that consists of feature extraction, similarity measure, and re-ranking of retrieved images. This method retrieves similar images of the query image from the database and re-ranks them according to the disparity map. The performance is evaluated using the metrics namely average retrieval precision (APR) and average retrieval rate (ARR). According to the performance outcomes, the multi-feature based image retrieval using Mahalanobis distance measure has produced better result compared to other distance measures namely Euclidean, Minkowski, the sum of absolute difference (SAD) and the sum of squared absolute difference (SSAD). Therefore, the stereo image retrieval systems presented has high potential in biomedical image storage and retrieval systems.

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.

scholarly journals Image salvage based on visual courtesy model using ROI

2018 ◽  
Vol 7 (2.26) ◽  
pp. 63
Author(s):  
K Deepa ◽  
K Priyanka
Keyword(s):  
Image Retrieval ◽  
Teacher Learning ◽  
Image Data ◽  
Saliency Map ◽  
Regions Of Interest ◽  
Content Based Image Retrieval ◽  
Matching Method ◽  
Visual Objects ◽  
Retrieval Systems ◽  
Definition Of

The process of demonstrating, organizing and evaluating the pictures regarding the information despite of evaluating pictures is the field of Content Based Image Retrieval (CBIR). Here we work on the salvage of images based not on keywords or explanations but on features haul out directly from the image data. The well-organized algorithms of salvage algorithms are already proposed. Content Based Image Retrieval has replaced Text Based Image Retrieval. CBIR is processed by more methods and research scientists are working to improve the accuracy of the technique. The project presents that the ROI from an image is retrieved and it retains the image based on Teacher Learning Based Optimization genetic algorithm. The retrieval of the image improves the efficiency based on two metrics such as precision and recall which is the main advantage of the project. The issue of Content Based Image Retrieval systems to provide the semantic gap and to determine the variation between the structure of visual objects and definition of semantics. From the human visual system the visual courtesy is more projected for the purpose of Content Based Image Retrieval. The new similarity based matching method is described based on the saliency map which retains the courtesy values and the regions of interest are hauled out. 

Image Retrieval Techniques Using Content-Based Local Binary Descriptors

2017 ◽  
pp. 173-195
Author(s):  
Rakesh Asery ◽  
Ramesh Kumar Sunkaria ◽  
Puneeta Marwaha ◽  
Lakhan Dev Sharma
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Gabor Transform ◽  
Comparative Experiment ◽  
Query Image ◽  
Binary Descriptors ◽  
Retrieval Rate ◽  
Retrieval Systems ◽  
Different Content

In this chapter authors introduces content-based image retrieval systems and compares them over a common database. For this, four different content-based local binary descriptors are described with and without Gabor transform in brief. Further Nth derivative descriptor is calculated using (N-1)th derivative, based on rotational and multiscale feature extraction. At last the distance based query image matching is used to find the similarity with database. The performance in terms of average precision, average retrieval rate, different orders of derivatives in the form of average retrieval rate, and length of feature vector v/s performance in terms of time have been calculated. For this work a comparative experiment has been conducted using the Ponce Group images on seven classes (each class have 100 images). In addition, the performance of the all descriptors have been analyzed by combining these with the Gabor transform.

scholarly journals Convolutional neural networks for relevance feedback in content based image retrieval

2020 ◽  
Vol 79 (37-38) ◽  
pp. 26995-27021
Author(s):  
Lorenzo Putzu ◽  
Luca Piras ◽  
Giorgio Giacinto
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Representation ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Data Sets ◽  
Great Success ◽  
Query Image ◽  
Pros And Cons ◽  
The One

Abstract Given the great success of Convolutional Neural Network (CNN) for image representation and classification tasks, we argue that Content-Based Image Retrieval (CBIR) systems could also leverage on CNN capabilities, mainly when Relevance Feedback (RF) mechanisms are employed. On the one hand, to improve the performances of CBIRs, that are strictly related to the effectiveness of the descriptors used to represent an image, as they aim at providing the user with images similar to an initial query image. On the other hand, to reduce the semantic gap between the similarity perceived by the user and the similarity computed by the machine, by exploiting an RF mechanism where the user labels the returned images as being relevant or not concerning her interests. Consequently, in this work, we propose a CBIR system based on transfer learning from a CNN trained on a vast image database, thus exploiting the generic image representation that it has already learned. Then, the pre-trained CNN is also fine-tuned exploiting the RF supplied by the user to reduce the semantic gap. In particular, after the user’s feedback, we propose to tune and then re-train the CNN according to the labelled set of relevant and non-relevant images. Then, we suggest different strategies to exploit the updated CNN for returning a novel set of images that are expected to be relevant to the user’s needs. Experimental results on different data sets show the effectiveness of the proposed mechanisms in improving the representation power of the CNN with respect to the user concept of image similarity. Moreover, the pros and cons of the different approaches can be clearly pointed out, thus providing clear guidelines for the implementation in production environments.

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