Machine Learning and Statistical Modeling Approaches to Image Retrieval

2004 ◽  
Keyword(s):  
Machine Learning ◽  
Image Retrieval ◽  
Statistical Modeling ◽  
Modeling Approaches

Linguistic Indexing of Images with Database Mediation

2011 ◽  
pp. 2420-2425
Author(s):  
Emmanuel Udoh
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Image Retrieval ◽  
Statistical Modeling ◽  
Digital Images ◽  
Statistical Error ◽  
Large Databases ◽  
Visual Aid ◽  
Active Research

Computer vision or object recognition complements human or biological vision using techniques from machine learning, statistics, scene reconstruction, indexing and event analysis. Object recognition is an active research area that implements artificial vision in software and hardware. Some application examples are autonomous robots, surveillance, indexing databases of pictures and human computer interaction. This visual aid is beneficial to users, because humans remember information with greater accuracy when it is presented visually than when it originates in writing, speech or in kinesthetic form. Linguistic indexing adds another dimension to computer vision by automatically assigning words or textual descriptions to images. This augments content-based image retrieval (CBIR) that extracts or searches for digital images in large databases. According to Li and Wang (2003), most of the existing CBIR projects are general-purpose image retrieval systems that search images visually similar to a query sketch. Current CBIR systems are incapable of assigning words automatically to images due to the inherent difficulty of recognizing numerous objects at once. This current situation is stimulating several research endeavors that seek to assign text to images, thereby improving image retrieval in large databases. To enhance information processing using object recognition techniques, current research has focused on automatic linguistic indexing of digital images (ALIDI). ALIDI requires a combination of mathematical, statistical, computational, and graphical backgrounds. Many researchers have focused on various aspects of linguistic processing such as CBIR (Ghosal, Ircing, & Khudanpur, 2005; Iqbal & Aggarwal, 2002, Wang, 2001) machine learning techniques (Iqbal & Aggarwal, 2002), digital library (Witen & Bainbridge, 2003) and statistical modeling (Li, Gray, & Olsen, 20004, Li & Wang, 2003). A growing approach is the utilization of statistical models as demonstrated by Li and Wang (2003). It entails building databases of images to be used for supervised learning. A trained system is used to recognize and identify new images with statistical error margin. This statistical modeling approach uses a hidden Markov model to extract representative information about any category of images analyzed. However, in using computer to recognize images with textual description, some of the researchers employ solely text-based approaches. In this article, the focus is on the computational and graphical aspects of ALIDI in a system that uses Web-based access in order to enable wider usage (Ntoulas, Chao, & Cho, 2005). This system uses image composition (primary hue and saturation) in the linguistic indexing of digital images or pictures.

Machine learning versus statistical modeling

2014 ◽  
Vol 56 (4) ◽  
pp. 588-593 ◽  
Author(s):  
Anne-Laure Boulesteix ◽  
Matthias Schmid
Keyword(s):  
Machine Learning ◽  
Statistical Modeling

Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform

2018 ◽  
Vol 23 (13) ◽  
pp. 4749-4764 ◽  
Author(s):  
Hong-ying Yang ◽  
Lin-lin Liang ◽  
Can Zhang ◽  
Xue-bing Wang ◽  
Pan-pan Niu ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Statistical Modeling ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform

A Machine Learning-Based Model for Content-Based Image Retrieval

2011 ◽  
pp. 230-251 ◽  
Author(s):  
Hakim Hacid ◽  
Abdelkader Djamel Zighed
Keyword(s):  
Machine Learning ◽  
Image Retrieval ◽  
Nearest Neighbors ◽  
Multimedia Databases ◽  
Similarity Criteria ◽  
Content Based Image Retrieval ◽  
Multidimensional Space ◽  
K Nearest Neighbors ◽  
Indexing Structure ◽  
Group Data

A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the k-Nearest Neighbors (k-NN) approach to retrieve databases. Due to some disadvantages of a such approach, the use of neighborhood graphs was proposed. This approach is interesting but it has some disadvantages consisting, mainly, in its complexity. This chapter presents a step in a long process of analyzing, structuring, and retrieving multimedia databases. Indeed, we propose an effective method for locally updating neighborhood graphs which constitute our multimedia index. Then, we exploit this structure in order to make easy and effective the retrieval process using queries in an image form in one hand. In another hand, we use the indexing structure to annotate images in order to describe their semantics. The proposed approach is based on an intelligent manner for locating points in a multidimensional space. Promising results are obtained after experimentations on various databases. Future issues of the proposed approach are very relevant in this domain.

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