scholarly journals Sketch Based Image Retrieval in Large Databases using Edge Features

2020 ◽  
Vol 8 (5) ◽  
pp. 4401-4405
Keyword(s):  
Image Retrieval ◽  
Retrieval Performance ◽  
Efficient Approach ◽  
Overlapping Grids ◽  
Large Databases ◽  
Similar Images ◽  
Edge Features

Sketch-based image retrieval (SBIR) presents better flexibility in expressing the query as sketch for retrieval of images as opposed to text based retrieval. Using a sketch, it is easier to express the orientation and pose of the objects for image retrieval from the database. We propose an efficient approach for SBIR from large databases based on hand awn rough sketch. In the proposed method, images are synthesized to yield a binary sketch that is processed in similar way to user drawn sketch. Edge features are extracted by overlaying the sketch with non-overlapping and overlapping grids, respectively. The most similar images to the query are then retrieved from the database using weighted based similarity approach. Experiments are performed on flickr15k dataset yielding excellent retrieval performance in comparison to the methods available in the literature.

scholarly journals A NEW APPROACH TO SHAPE-BASED IMAGE RETRIEVAL

2014 ◽  
pp. 99-106
Author(s):  
Mehdi Chehel Amirani ◽  
Zahra Sadeghi Gol ◽  
Ali Asghar Beheshti Shirazi
Keyword(s):  
Image Retrieval ◽  
Curvature Function ◽  
Feature Points ◽  
Retrieval Performance ◽  
New Approach ◽  
Large Databases ◽  
Indexing And Retrieval ◽  
Active Research ◽  
Low Dimensional ◽  
Object Indexing

Content-based image retrieval (CBIR) is very active research topic in recent years. This paper introduces a new approach to shape-based image retrieval. At first, feature points are determined at the boundary of the shape as the extremums of a new version of the curvature function and the initial features are calculated at these points. The proposed method utilizes a supervised system for nonlinear combination of initial features for extraction of efficient and low dimensional feature vector for each shape. The retrieval performance of the approach is illustrated using the MPEG-7 shape database. Our experiments show that the proposed method is well suited for object indexing and retrieval in large databases.

EXTRACTING SALIENT REGIONS AND LEARNING IMPORTANCE SCORES IN REGION-BASED IMAGE RETRIEVAL

2003 ◽  
Vol 17 (08) ◽  
pp. 1349-1367 ◽  
Author(s):  
BYOUNGCHUL KO ◽  
HYERAN BYUN
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Retrieval Performance ◽  
Matching Process ◽  
Similar Images ◽  
Salient Regions ◽  
Feedback Method ◽  
Successful Retrieval ◽  
Feedback Algorithm

In this paper, we propose a new method for extracting salient regions and learning their importance scores in region-based image retrieval. In Region-Based Image Retrieval (RBIR), not all the regions are important for retrieving similar images and rather, in retrieval, the user is often interested in performing a query on only one or a few regions rather than the whole image. Therefore, for a successful retrieval system, it is an important issue to specify which regions are important for retrieving an image. To extract salient regions from images automatically, we make three assumptions and determine salient regions with their importance scores. In this paper, we apply the relevance feedback algorithm to the matching process as two different purposes: one is for updating importance scores of salient regions and the other is for updating weights of feature vectors. By using our relevance feedback method, the matching process can improve retrieval performance interactively and allow progressive refinement of query results according to the user's feedback action. Through experiments and comparison with other methods, our proposed method shows good performance as well as easy and semantic interface for region-based image retrieval. The efficacy of our method is validated using a set of 3000 images from Corel-photo CD.

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.

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.

scholarly journals Object Based Image Retrieval from a Repository

2019 ◽  
Vol 9 (2S) ◽  
pp. 550-554
Keyword(s):  
Image Retrieval ◽  
World Wide ◽  
Storage Capacity ◽  
Imaging System ◽  
Digital World ◽  
Digital Storage ◽  
Object Properties ◽  
Object Based ◽  
Similar Images ◽  
Similar Kind

Today is a digital world. Due to the increase in imaging system, digital storage capacity and internetworking technology Content Based Retrieval of Images (CBIR) has become a vibrant research spot. The CBIR systems helps user to browse and retrieve similar kind of images from huge databases and World Wide Web. The Object based Image Retrieval (OBIR) Systems are the extension to the CBIR technique where it retrieves the similar images based on the object properties. So far massive amount of work has been done in this field of research. A plenty of the techniques and algorithms are published in the different papers. This paper provides brief survey on basic and recent approaches and techniques explained in different papers.

scholarly journals Content based Image Retrieval System using Combination of Color and Shape Features, and Siamese Neural Network

2019 ◽  
Vol 9 (2S) ◽  
pp. 71-77
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Retrieval System ◽  
Image Features ◽  
Shape Features ◽  
Retrieval Performance ◽  
Novel Approach ◽  
Learning Technique ◽  
Image Retrieval System ◽  
Similar Images

With an advent of technologya huge collection of digital images is formed as repositories on world wide web (WWW). The task of searching for similar images in the repository is difficult. In this paper, retrieval of similar images from www is demonstrated with the help of combination of image features as color and shape and then using Siamese neural network which is constructed to the requirement as a novel approach. Here, one-shot learning technique is used to test the Siamese Neural Network model for retrieval performance. Various experiments are conducted with both the methods and results obtained are tabulated. The performance of the system is evaluated with precision parameter and which is found to be high.Also, relative study is made with existing works.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 281-302
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

A document expansion framework for tag-based image retrieval

2018 ◽  
Vol 70 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Wei Lu ◽  
Heng Ding ◽  
Jiepu Jiang
Keyword(s):  
Image Retrieval ◽  
Nearest Neighbor ◽  
Image Representation ◽  
Image Features ◽  
Mobile Platforms ◽  
Content Type ◽  
Expansion Strategy ◽  
Rank List ◽  
Core Components ◽  
Similar Images

Purpose The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR). Design/methodology/approach The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate). Findings The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|RD) is able to erase the negative influences of error images in RD. Research limitations/implications Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach. Originality/value Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.

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