Neighbourhood search feature selection method for content-based mammogram retrieval

Chandy, D. Abraham; Christinal, Hepzibah A.; Theodore, Alwyn John; Selvan, S.

doi:10.1007/s11517-016-1513-x

Cited by 12 publications

(6 citation statements)

References 31 publications

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“…As a result, there has been an increasing amount of research in recent years on interacting with computers through multiple modalities, such as simple voice, gesture, and expression interaction. This human-like interaction has opened up a new generation of humancomputer interaction research [13][14][15]. The main point to note is that the connection between humans and computers has a certain natural and social character.…”

Section: Introductionmentioning

confidence: 99%

Research on Semisupervised Affective Interaction Technology in Immersive Virtual Teaching Environment

Tang

Zhang

2022

Wireless Communications and Mobile Computing

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The separation of time and space in immersive virtual teaching makes students unable to realize emotional communication, which may affect students’ mental health. In recent years, the use of affective computing technology to solve the problem of affective loss in distance education has become a key research topic. In order to realize the problem of emotion interaction in immersive virtual teaching, a semisupervised support vector machine- (SVM-) based affective interaction model was proposed. First, the natural language sequences of students in the virtual teaching environment are preprocessed using a statistical-based framing method, and mutual information and expected cross-entropy are used as feature selection methods. Then, a vector space model based on TF/IDF feature term weights is proposed to implement the feature vector representation of natural language sequences. Finally, after the constructed sentiment space, a semisupervised SVM is employed as the classifier to complete the affective interaction computation. The experimental results of emotion classification show that the proposed model is able to determine and understand the emotional state more accurately than other traditional models and significantly improves the training speed. In addition, the proposed model can provide emotional encouragement or emotional compensation according to the specific emotional state of the learner.

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Section: Introductionmentioning

confidence: 99%

Research on Semisupervised Affective Interaction Technology in Immersive Virtual Teaching Environment

Tang

Zhang

2022

Wireless Communications and Mobile Computing

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“…Especially in the application of natural scenes, because the background is extremely complex, the font size varies and is affected by special circumstances such as overlapping and pollution of various perspectives, and the difficulty of text detection in natural scenes in videos is much greater than that in ordinary scenes. Image content can be divided into two parts: perceptual content and semantic content [7][8][9]. e visual part of the image, including the direct visual impression of color, shape, and texture, is the perceptual content.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection Method of English Text in Natural Scenes in Video

Dai

Chen

2021

Scientific Programming

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With the rapid development of Internet technology, breakthroughs have been made in all branches of computer vision. Especially in image detection and target tracking, deep learning techniques such as convolutional neural networks have achieved excellent results. In order to explore the applicability of machine learning technology in the field of video text recognition and extraction, a YOLOv3 network based on multiscale feature transformation and migration fusion is proposed to improve the accuracy of english text detection in natural scenes in video. Firstly, aiming at the problem of multiscale target detection in video key frames, based on the YOLOv3 network, the scale conversion module of STDN algorithm is used to reduce the low-level feature map, and a backbone network with feature reuse is constructed to extract features. Then, the scale conversion module is used to enlarge the high-level feature map, and a feature pyramid network (FPN) is built to predict the target. Finally, the improved YOLOv3 network is verified to extract key text from images. The experimental results show that the improved YOLOv3 network can effectively improve the false detection and missed detection caused by occlusion and small target, and the accuracy of English text extraction is obviously improved.

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“…For evidence-based medicine, there is a urgent need to access relevant images from a large medical database to support the diagnosis of certain diseases [12,22,23]. Mammography is a screening tool that is used to localize and identify the suspicious tissues [13].…”

Section: Introductionmentioning

confidence: 99%

Mammogram Image Retrieval using Ipso Optimized Anfis Classifier

2019

IJITEE

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Content-based image retrieval (CBIR) is an research area over the past years that has attracted research. In various medical applications like mammogram analysis CBIR techniques helps the medical team to get similar set of images from a large medical records to help in diagnosis of a disease. This paper proposes an efficient Content-Based Mammogram Image Retrieval method by using an Optimized Classifier. Initially, the input dataset is preprocessed, in which noise removal and contrast enhancement are done. Next, pectoral muscles of the mammogram images are removed using Single Sided Edge Marking (SSEM). Now, feature extraction is done, in which GLCM features, Gabor features and the Local Pattern with Binary features are being removed. The features that are being removed are classified into three classes namely benign, malignant and normal. An optimized classifier named as Adaptive Neuro Fuzzy Inference System (ANFIS), which is optimized by using the Improved Particle Swarm Optimization (IPSO) technique, is used for classification purpose. Finally, similarity is assessed between the trained feature distance vectors and the feature distance vectors of the input query image.Similarity assessment is done using Euclidean Distance metric and the image that has the lowest distance compared with the query is retrieved. The experimental results are obtained for the proposed system and they are compared with the existing techniques.

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Neighbourhood search feature selection method for content-based mammogram retrieval

Cited by 12 publications

References 31 publications

Research on Semisupervised Affective Interaction Technology in Immersive Virtual Teaching Environment

Research on Semisupervised Affective Interaction Technology in Immersive Virtual Teaching Environment

Intelligent Detection Method of English Text in Natural Scenes in Video

Mammogram Image Retrieval using Ipso Optimized Anfis Classifier

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