A simple and efficient video image clustering algorithm for person specific query and image retrieval

2009 ◽  
Author(s):  
Md. Shafaeat Hossain ◽  
Khandaker A. Rahman ◽  
Md. Hasanuzzaman ◽  
Vir V. Phoha
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Video Image ◽  
Image Clustering ◽  
Efficient Video

An Image Clustering and Feedback-Based Retrieval Framework

2012 ◽  
pp. 62-80
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Image Region ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

An Image Clustering and Feedback-based Retrieval Framework

2010 ◽  
Vol 1 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Single Object ◽  
Image Region ◽  
Retrieval Accuracy ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

Image Retrieval via CURE Clustering and SIFT Algorithms

2013 ◽  
Vol 303-306 ◽  
pp. 1573-1576 ◽  
Author(s):  
Xian Tan
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Text Clustering ◽  
Image Clustering ◽  
Original Text ◽  
Retrieval Performance ◽  
Fusion Model ◽  
Semantic Clustering ◽  
Model Image ◽  
High Level

This paper, by using the short CURE clustering algorithm and image SIFT identification method, the establishment of a kind of image semantic clustering fusion model (image text clustering fusion model, referred to as ITCFM). The model is based on component method, the original image components, original text member, image clustering member, text clustering components, clustering fusion member five parts. In ITCM model for image semantic clustering characteristics on the basis of the description and extraction. The experimental results show that ITCM model can effectively to image to describe the high-level semantic, the image retrieval effect is good, and have stable retrieval performance.

Factor-Bounded Nonnegative Matrix Factorization

2021 ◽  
Vol 15 (6) ◽  
pp. 1-18
Author(s):  
Kai Liu ◽  
Xiangyu Li ◽  
Zhihui Zhu ◽  
Lodewijk Brand ◽  
Hua Wang
Keyword(s):  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Optimization Methods ◽  
Auxiliary Function ◽  
Image Clustering ◽  
Real World Datasets ◽  
The Relationship ◽  
Matrix Factors

Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems. Traditional optimization methods, such as the Multiplicative Updating Algorithm (MUA), solves the NMF problem by utilizing an auxiliary function to ensure that the objective monotonically decreases. Although the objective in MUA converges, there exists no proof to show that the learned matrix factors converge as well. Without this rigorous analysis, the clustering performance and stability of the NMF algorithms cannot be guaranteed. To address this knowledge gap, in this article, we study the factor-bounded NMF problem and provide a solution algorithm with proven convergence by rigorous mathematical analysis, which ensures that both the objective and matrix factors converge. In addition, we show the relationship between MUA and our solution followed by an analysis of the convergence of MUA. Experiments on both toy data and real-world datasets validate the correctness of our proposed method and its utility as an effective clustering algorithm.

Study on Enhancing Terminal Identification in Track and Field Using Digital X-Ray Photography Image

2014 ◽  
Vol 989-994 ◽  
pp. 3851-3855
Author(s):  
Guang Jin Lai
Keyword(s):  
Motion Capture ◽  
Clustering Algorithm ◽  
Digital Signal ◽  
Human Head ◽  
Track And Field ◽  
Image Clustering ◽  
Digital Signals ◽  
Recognition Method ◽  
One Dimensional ◽  
X Ray

Digital X-ray photography technology is under the control of the computer, to use one-dimensional or 2D X-ray detector to convert the captured image into digital signals directly to using image processing technology. It can realize the function of image analysis. We introduce X-ray photography technology into the terminal identification in track and field, and use the clustering algorithm to improve computer image clustering algorithm. Through capturing the digital signal of human head, arms and legs, it enhances the terminal recognition method in track and field. Finally we use MATLAB to calculate the captured image value of X-ray photography. Through calculation, motion capture and recognition of X-ray image are enhanced obviously. It provides a theoretical basis for researching on motion capture technology in track and field.

scholarly journals Image Retrieval Using Data Mining Technique

2020 ◽  
pp. 2115-2125
Author(s):  
Sarmad T. Abdul-Samad ◽  
Sawsan Kamal
Keyword(s):  
Image Retrieval ◽  
User Satisfaction ◽  
Clustering Algorithm ◽  
Retrieval System ◽  
Spatial Information ◽  
Search Space ◽  
Extraction Process ◽  
Retrieval Process ◽  
Data Mining Technique ◽  
Image Retrieval System

Even though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show that using the spatial features increases the system accuracy and that the clustering algorithm speeds up the image retrieval process. This shows that the proposed system works with texture and non-texture images.  

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