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.

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.

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 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 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 Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

Combining Block DCV and Support Vector Machine for Ear Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 36-44 ◽  
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

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