Mining association rules between low-level image features and high-level concepts

2001 ◽  
Author(s):  
Ishwar K. Sethi ◽  
Ioana L. Coman ◽  
Daniela Stan
Keyword(s):  
Association Rules ◽  
Image Features ◽  
Low Level ◽  
Mining Association Rules ◽  
High Level

Adaptive Feature Selection for Ultrasound Image Processing and its Application to Liver Cirrhosis Detection

2013 ◽  
Vol 411-414 ◽  
pp. 1372-1376
Author(s):  
Wei Tin Lin ◽  
Shyi Chyi Cheng ◽  
Chih Lang Lin ◽  
Chen Kuei Yang
Keyword(s):  
Clustering Algorithm ◽  
Ultrasound Image ◽  
Image Features ◽  
Ultrasound Images ◽  
Analytic Hierarchy ◽  
Low Level ◽  
Adaptive Feature Selection ◽  
High Level ◽  
Objects Detection ◽  
Hierarchy Process

An approach to improve the regions of interesting (ROIs) selection accuracy automatically for medical images is proposed. The aim of the study is to select the most interesting regions of image features that good for diffuse objects detection or classification. We use the AHP (Analytic Hierarchy Process) to obtain physicians high-level diagnosis vectors and are clustered using the well-known K-Means clustering algorithm. The system also automatically extracts low-level image features for improving to detect liver diseases from ultrasound images. The weights of low-level features are adaptively updated according the feature variances in the class. Finally, the high-level diagnosis decision is made based on the high-level diagnosis vectors for the top K near neighbors from the medical experts classified database. Experimental results show the effectiveness of the system.

scholarly journals Image Searching System

2021 ◽  
Vol 6 (2) ◽  
pp. 161-167
Author(s):  
Eduard Yakubchykt ◽  
◽  
Iryna Yurchak
Keyword(s):  
Semantic Content ◽  
Image Features ◽  
Multimedia Databases ◽  
Semantic Level ◽  
Low Level ◽  
Essential Properties ◽  
Calculation Algorithms ◽  
Similar Images ◽  
Definition Of ◽  
High Level

Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.

A Multi-Label Image Annotation With Multi-Level Tagging System

2018 ◽  
pp. 28-47
Author(s):  
Kalaivani Anbarasan ◽  
Chitrakala S.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Annotation ◽  
Image Features ◽  
Semantic Gap ◽  
Low Level ◽  
Image Retrieval System ◽  
Multi Level ◽  
High Level ◽  
Tagging System

The content based image retrieval system retrieves relevant images based on image features. The lack of performance in the content based image retrieval system is due to the semantic gap. Image annotation is a solution to bridge the semantic gap between low-level content features and high-level semantic concepts Image annotation is defined as tagging images with a single or multiple keywords based on low-level image features. The major issue in building an effective annotation framework is the integration of both low level visual features and high-level textual information into an annotation model. This chapter focus on new statistical-based image annotation model towards semantic based image retrieval system. A multi-label image annotation with multi-level tagging system is introduced to annotate image regions with class labels and extract color, location and topological tags of segmented image regions. The proposed method produced encouraging results and the experimental results outperformed state-of-the-art methods

Content-Based Image Retrieval

2011 ◽  
pp. 744-749
Author(s):  
Alan Wee-Chung Liew ◽  
Ngai-Fong Law
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Image Features ◽  
User Preferences ◽  
Multimedia Systems ◽  
Content Based Image Retrieval ◽  
Top Down ◽  
Low Level ◽  
Feature Based ◽  
High Level

With the rapid growth of Internet and multimedia systems, the use of visual information has increased enormously, such that indexing and retrieval techniques have become important. Historically, images are usually manually annotated with metadata such as captions or keywords (Chang & Hsu, 1992). Image retrieval is then performed by searching images with similar keywords. However, the keywords used may differ from one person to another. Also, many keywords can be used for describing the same image. Consequently, retrieval results are often inconsistent and unreliable. Due to these limitations, there is a growing interest in content-based image retrieval (CBIR). These techniques extract meaningful information or features from an image so that images can be classified and retrieved automatically based on their contents. Existing image retrieval systems such as QBIC and Virage extract the so-called low-level features such as color, texture and shape from an image in the spatial domain for indexing. Low-level features sometimes fail to represent high level semantic image features as they are subjective and depend greatly upon user preferences. To bridge the gap, a top-down retrieval approach involving high level knowledge can complement these low-level features. This articles deals with various aspects of CBIR. This includes bottom-up feature- based image retrieval in both the spatial and compressed domains, as well as top-down task-based image retrieval using prior knowledge.

scholarly journals Scene semantics involuntarily guide attention during visual search

2019 ◽  
Author(s):  
Taylor R. Hayes ◽  
John M. Henderson
Keyword(s):  
Visual Search ◽  
Search Task ◽  
Visual Search Task ◽  
Image Features ◽  
Semantic Features ◽  
Low Level ◽  
Overt Attention ◽  
Scene Viewing ◽  
High Level ◽  
Task Goals

During scene viewing, is attention primarily guided by low-level image salience or by high-level semantics? Recent evidence suggests that overt attention in scenes is primarily guided by semantic features. Here we examined whether the attentional priority given to meaningful scene regions is involuntary. Participants completed a scene-independent visual search task in which they searched for superimposed letter targets whose locations were orthogonal to both the underlying scene semantics and image salience. Critically, the analyzed scenes contained no targets, and participants were unaware of this manipulation. We then directly compared how well the distribution of semantic features and image salience accounted for the overall distribution of overt attention. The results showed that even when the task was completely independent from the scene semantics and image salience, semantics explained significantly more variance in attention than image salience and more than expected by chance. This suggests that salient image features were effectively suppressed in favor of task goals, but semantic features were not suppressed. The semantic bias was present from the very first fixation and increased non-monotonically over the course of viewing. These findings suggest that overt attention in scenes is involuntarily guided by scene semantics.

scholarly journals Deep saliency models learn low-, mid-, and high-level features to predict scene attention

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Taylor R. Hayes ◽  
John M. Henderson
Keyword(s):  
Image Features ◽  
Interaction Patterns ◽  
Low Level ◽  
Mixed Effects Modeling ◽  
Current State ◽  
Saliency Model ◽  
Image Saliency ◽  
High Level ◽  
Scene Features ◽  
Benchmarking Performance

AbstractDeep saliency models represent the current state-of-the-art for predicting where humans look in real-world scenes. However, for deep saliency models to inform cognitive theories of attention, we need to know how deep saliency models prioritize different scene features to predict where people look. Here we open the black box of three prominent deep saliency models (MSI-Net, DeepGaze II, and SAM-ResNet) using an approach that models the association between attention, deep saliency model output, and low-, mid-, and high-level scene features. Specifically, we measured the association between each deep saliency model and low-level image saliency, mid-level contour symmetry and junctions, and high-level meaning by applying a mixed effects modeling approach to a large eye movement dataset. We found that all three deep saliency models were most strongly associated with high-level and low-level features, but exhibited qualitatively different feature weightings and interaction patterns. These findings suggest that prominent deep saliency models are primarily learning image features associated with high-level scene meaning and low-level image saliency and highlight the importance of moving beyond simply benchmarking performance.

Based on the Semantics of the Low-Level Visual Features Image Retrieval

2012 ◽  
Vol 482-484 ◽  
pp. 512-517
Author(s):  
Xian Wen Zeng ◽  
Xue Dong Shen
Keyword(s):  
Image Retrieval ◽  
Input Parameter ◽  
Paper Analysis ◽  
Image Features ◽  
Visual Features ◽  
Color Feature ◽  
Semantic Image Retrieval ◽  
Low Level ◽  
High Level

This paper analysis the reasons that traditional CBIR can’t support based Semantic image retrieval, and gave a kind of method that Using SVM may solute it. Through studying and Classification, combining HSV Color feature as input parameter ,it realized the connection and map between the high-level semantics and low-level image features .Using this method to retrieve can have proved to get higher accuracy.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Determinants of Abuse of the Bodies of the Dead

2019 ◽  
pp. 71-77
Author(s):  
Margarita Khomyakova
Keyword(s):  
Low Income ◽  
Criminal Code ◽  
Value Judgments ◽  
Legal Norms ◽  
Low Level ◽  
Medical Institutions ◽  
Specific Object ◽  
The Subject ◽  
The Dead ◽  
High Level

The author analyzes definitions of the concepts of determinants of crime given by various scientists and offers her definition. In this study, determinants of crime are understood as a set of its causes, the circumstances that contribute committing them, as well as the dynamics of crime. It is noted that the Russian legislator in Article 244 of the Criminal Code defines the object of this criminal assault as public morality. Despite the use of evaluative concepts both in the disposition of this norm and in determining the specific object of a given crime, the position of criminologists is unequivocal: crimes of this kind are immoral and are in irreconcilable conflict with generally accepted moral and legal norms. In the paper, some views are considered with regard to making value judgments which could hardly apply to legal norms. According to the author, the reasons for abuse of the bodies of the dead include economic problems of the subject of a crime, a low level of culture and legal awareness; this list is not exhaustive. The main circumstances that contribute committing abuse of the bodies of the dead and their burial places are the following: low income and unemployment, low level of criminological prevention, poor maintenance and protection of medical institutions and cemeteries due to underperformance of state and municipal bodies. The list of circumstances is also open-ended. Due to some factors, including a high level of latency, it is not possible to reflect the dynamics of such crimes objectively. At the same time, identification of the determinants of abuse of the bodies of the dead will reduce the number of such crimes.

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