Image Retrieval with Relevance Feedback using SVM Active Learning

2016 ◽  
Vol 6 (6) ◽  
pp. 3238 ◽  
Author(s):  
Giang Truong Ngo ◽  
Tao Quoc Ngo ◽  
Dung Duc Nguyen
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Effective Feedback ◽  
General Relevance ◽  
High Level ◽  
Widespread Interest

<pre>In content-based image retrieval, relevant feedback is studied extensively <br />to narrow the gap between low-level image feature and high-level semantic <br />concept. In general, relevance feedback aims to improve the retrieval <br />performance by learning with user's <span>judgements</span> on the retrieval results. <br />Despite widespread interest, but feedback related technologies are often <br />faced with a few limitations. One of the most obvious limitations is often <br />requiring the user to repeat a number of steps before obtaining the <br />improved search results. This makes the process inefficient and tedious <br />search for the online applications. In this paper, a effective feedback <br />related scheme for content-based image retrieval is proposed. First, a <br />decision boundary is learned via Support Vector Machine to filter the <br />images in the database. Then, a ranking function for selecting the most <br />informative samples will be calculated by defining a novel criterion that <br />considers both the scores of Support Vector Machine function and similarity<br />metric between the "ideal query" and the images in the database. The <br />experimental results on standard <span>datasets</span> have showed the effectiveness <br />of the proposed method.</pre>

scholarly journals Image Retrieval with Relevance Feedback using SVM Active Learning

2016 ◽  
Vol 6 (6) ◽  
pp. 3238
Author(s):  
Giang Truong Ngo ◽  
Tao Quoc Ngo ◽  
Dung Duc Nguyen
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Effective Feedback ◽  
General Relevance ◽  
High Level ◽  
Widespread Interest

<pre>In content-based image retrieval, relevant feedback is studied extensively <br />to narrow the gap between low-level image feature and high-level semantic <br />concept. In general, relevance feedback aims to improve the retrieval <br />performance by learning with user's <span>judgements</span> on the retrieval results. <br />Despite widespread interest, but feedback related technologies are often <br />faced with a few limitations. One of the most obvious limitations is often <br />requiring the user to repeat a number of steps before obtaining the <br />improved search results. This makes the process inefficient and tedious <br />search for the online applications. In this paper, a effective feedback <br />related scheme for content-based image retrieval is proposed. First, a <br />decision boundary is learned via Support Vector Machine to filter the <br />images in the database. Then, a ranking function for selecting the most <br />informative samples will be calculated by defining a novel criterion that <br />considers both the scores of Support Vector Machine function and similarity<br />metric between the "ideal query" and the images in the database. The <br />experimental results on standard <span>datasets</span> have showed the effectiveness <br />of the proposed method.</pre>

Medical Image Retrieval Based on Semi-Supervised Learning

2010 ◽  
Vol 108-111 ◽  
pp. 201-206 ◽  
Author(s):  
Hui Liu ◽  
Cai Ming Zhang ◽  
Hua Han
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Image Retrieval ◽  
Supervised Learning ◽  
Relevance Feedback ◽  
Medical Image ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Learning Support ◽  
Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

scholarly journals Relevance Feedback berdasarkan Support Vector Machine pada Content Based Image Retrieval

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Virginia Tulenan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Relevant Feedback

Content based image retrieval adalah bidang penelitianyang sangat penting saat ini dalam bidang multimedia database.Banyak penelitian yang telah dilakukan dalam dekade terakhiruntuk merancang teknik image retrieval yang efisien dari imagedatabase. Meskipun banyak teknik pengindeksan dan retrievaltelah dikembangkan, namun masih belum terdapat teknikpemisahan ciri (feature extraction), indexing dan retrieval yangbisa diterima secara universal oleh semua orang. Dalam tulisanini, digunakanlah metode relevant feedback berdasarkan supportvector machine (SVM) dan muhalobis distance untuk pengukurankemiripan pada image retrieval.

Research on Related Technology of Content-Based Image Retrieval

2013 ◽  
Vol 448-453 ◽  
pp. 3616-3620
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Bai Chuan Li
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
The Novel ◽  
Effective Solution ◽  
Retrieval Accuracy ◽  
Novel Method ◽  
Feedback Method ◽  
High Level

Content-Based Image Retrieval (CBIR) system existed a gap between high-level concepts and low-level features. As an effective solution, the Relevance Feedback (RF) technique has been used on many CBIR systems to improve the retrieval precision. In order to further improve convergence speed and retrieval accuracy, a novel relevance feedback method was proposed. According to feedback from user, image feature was weighted and adjusted in the novel method.

scholarly journals Modified one-class support vector machine for content-based image retrieval with relevance feedback

2018 ◽  
Vol 5 (1) ◽  
pp. 1541702
Author(s):  
Oluwole A. Adegbola ◽  
David O. Aborisade ◽  
Segun I. Popoola ◽  
Olatide A. Amole ◽  
Aderemi A. Atayero ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Content Based Image Retrieval ◽  
Support Vector

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.

Sign in / Sign up

Export Citation Format

Share Document