scholarly journals Content-Based Image Retrieval : A Comprehensive Study

2019 ◽  
pp. 1073-1081
Author(s):  
Er Aman ◽  
Amit Rawat ◽  
Ashwin Giri ◽  
Hardik Gothwal
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Empirical Studies ◽  
Content Based Image Retrieval ◽  
Recent Success ◽  
Long Run ◽  
Learning Techniques ◽  
Depth Analysis ◽  
High Level ◽  
Learning Options

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.

scholarly journals A Review on Content Based Image Retrieval System Features derived by Deep Learning Models

2021 ◽  
Vol 9 (12) ◽  
pp. 42-57
Author(s):  
Mr. Kommu Naveen
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Network Models ◽  
Classification Problem ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Usual Procedure ◽  
Image Retrieval System ◽  
High Level ◽  
Trained Network

Abstract: In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image, and retrieve images which have similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally the choice of these features play a very important role in the success of this system, and high level features are required to reduce the “semantic gap”. In this paper, we propose to use features derived from pre-trained network models from a deep- learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method, and also propose a pre-clustering of the database based on the above-mentioned features which yields comparable results in a much shorter time in most of the cases. Keywords Content Based Image Retrieval Feature Selection Deep Learning Pre-trained Network Models Pre-clustering

scholarly journals Deep Learning for Content-Based Image Retrieval

2014 ◽  
Author(s):  
Ji Wan ◽  
Dayong Wang ◽  
Steven Chu Hong Hoi ◽  
Pengcheng Wu ◽  
Jianke Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Content Based Image Retrieval

scholarly journals Combined global and local semantic feature–based image retrieval analysis with interactive feedback

2019 ◽  
Vol 53 (1-2) ◽  
pp. 3-17
Author(s):  
A Anandh ◽  
K Mala ◽  
R Suresh Babu
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Research Work ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Retrieval System ◽  
False Image ◽  
Active Research ◽  
High Level ◽  
Interactive Feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

scholarly journals Bag-of-Words Representation in Image Annotation: A Review

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Chih-Fong Tsai
Keyword(s):  
Image Retrieval ◽  
Image Annotation ◽  
Classification Problem ◽  
Feature Representation ◽  
Future Research ◽  
Bag Of Words ◽  
Low Level ◽  
Learning Techniques ◽  
Class Labels ◽  
High Level

Content-based image retrieval (CBIR) systems require users to query images by their low-level visual content; this not only makes it hard for users to formulate queries, but also can lead to unsatisfied retrieval results. To this end, image annotation was proposed. The aim of image annotation is to automatically assign keywords to images, so image retrieval users are able to query images by keywords. Image annotation can be regarded as the image classification problem: that images are represented by some low-level features and some supervised learning techniques are used to learn the mapping between low-level features and high-level concepts (i.e., class labels). One of the most widely used feature representation methods is bag-of-words (BoW). This paper reviews related works based on the issues of improving and/or applying BoW for image annotation. Moreover, many recent works (from 2006 to 2012) are compared in terms of the methodology of BoW feature generation and experimental design. In addition, several different issues in using BoW are discussed, and some important issues for future research are discussed.

scholarly journals A Proposal of Educational Video Gamification as a Service

2019 ◽  
Author(s):  
Daniel de Sousa Moraes ◽  
Álan L. V. Guedes ◽  
Antonio J. G. Busson ◽  
Carlos de Salles Soares Neto ◽  
Sérgio Colcher
Keyword(s):  
Deep Learning ◽  
Online Course ◽  
Educational Video ◽  
Hybrid Courses ◽  
Student Centered ◽  
Massive Open Online Course ◽  
Learning Techniques ◽  
High Level ◽  
Khan Academy ◽  
Storage Distribution

Online and hybrid courses are characterized by the use of technologies teaching support and student-centered methodologies. In particular, the use of content authoring, storage, distribution, and presentation technologies has contributed to the emergence of Virtual Learning Environments (VLEs), such as Moodle, and MOOCS (Massive Open Online Course), such as EdX. However, those types of courses still suffers from problems involving student engagement. To improve this engagement, this article discusses the proposal of a Deep Learning as a Service (DLaS) called EVGAS (Educational Video Gamification As a Service). More precisely, EVGAS is a service for recommending and gamifying activities in existing educational VLE videos. First, EVGAS uses Deep Learning techniques to classify videos from an AVA. Then, it accesses repositories of activities, such as Khan Academy and UVA OnlineJudge, in order to select activities according to the topics classified from the videos. Finally, EVGAS adds VLA activities and gamification. It allows the teacher to monitor student progress, including in relation to each topic of the video. As a result, this paper presents high level requirements and an EVGAS Mockup.

scholarly journals Automatic content-based image retrieval using hierarchical clustering algorithms

2021 ◽  
Author(s):  
Kambiz Jarrah
Keyword(s):  
Image Retrieval ◽  
Hierarchical Clustering ◽  
Relevance Feedback ◽  
Clustering Algorithms ◽  
User Interaction ◽  
Content Based Image Retrieval ◽  
Feature Weight ◽  
High Level ◽  
Tree Map ◽  
Self Organizing

The overall objective of this thesis is to present a methodology for guiding adaptations of an RBF-based relevance feedback network, embedded in automatic content-based image retrieval (CBIR) systems, through the principle of unsupervised hierarchical clustering. The main focus of this thesis is two-fold: introducing a new member of Self-Organizing Tree Map (SOTM) family, the Directed self-organizing tree map (DSOTM) that not only provides a partial supervision on cluster generation by forcing divisions away from the query class, but also presents an objective verdict on resemblance of the input pattern as its tree structure grows; and using a base-10 Genetic Algorithm (GA) approach to accurately determine the contribution of individual feature vectors for a successful retrieval in a so-called "feature weight detection process." The DSOTM is quite attractive in CBIR since it aims to reduce both user workload and subjectivity. Repetitive user interaction steps are replaced by a DSOTM module, which adaptively guides relevance feedback, to bridge the gap between low-level image descriptors and high-level semantics. To further reduce this gap and achieve an enhanced performance for the automatic CBIR system under study, a GA-based approach was proposed in conjunction with the DSOTM. The resulting framework is referred to as GA-based CBIR (GA-CBIR) and aims to import human subjectivity by automatically adjusting the search process to what the system evolves "to believe" is significant content within the query. In this engine, traditional GA operators work closely with the DSOTM to better attune the apparent discriminative characteristics observed in an image by a human user.

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