Non-parametric similarity measures for unsupervised texture segmentation and image retrieval

2002 ◽  
Author(s):  
J. Puzicha ◽  
T. Hofmann ◽  
J.M. Buhmann
Keyword(s):  
Image Retrieval ◽  
Similarity Measures ◽  
Texture Segmentation ◽  
Non Parametric

Content Based Image Retrieval Using Active-Nets

2011 ◽  
pp. 85-114 ◽  
Author(s):  
David García Pérez ◽  
Antonio Mosquera ◽  
Stefano Berretti ◽  
Alberto Del Bimbo
Keyword(s):  
Image Retrieval ◽  
Structural Information ◽  
Similarity Measures ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Objects ◽  
Image Retrieval System ◽  
Deformable Structure ◽  
Retrieval Problem ◽  
Active Research

Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of retrieval, but the large part of these works have focused on sub-parts of the retrieval problem, providing targeted solutions only for individual aspects (i.e., feature extraction, similarity measures, indexing, etc). In this chapter, we first shortly review some of the main practiced solutions for content-based image retrieval evidencing some of the main issues. Then, we propose an original approach for the extraction of relevant image objects and their matching for retrieval applications, and present a complete image retrieval system which uses this approach (including similarity measures and image indexing). In particular, image objects are represented by a two-dimensional deformable structure, referred to as “active net.” Active net is capable of adapting to relevant image regions according to chromatic and edge information. Extension of the active nets has been defined, which permits the nets to break themselves, thus increasing their capability to adapt to objects with complex topological structure. The resulting representation allows a joint description of color, shape, and structural information of extracted objects. A similarity measure between active nets has also been defined and used to combine the retrieval with an efficient indexing structure. The proposed system has been experimented on two large and publicly available objects databases, namely, the ETH-80 and the ALOI.

Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

2020 ◽  
Vol 10 (1) ◽  
pp. 1-23
Author(s):  
Maher Alrahhal ◽  
Supreethi K.P.
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Retrieval System ◽  
Similarity Measures ◽  
Visual Features ◽  
Classification Problems ◽  
Bag Of Features ◽  
Image Retrieval System ◽  
Multimedia Image ◽  
Medical Dataset

The authors propose WNAHVF, a combined weighted and normalized AlexNet with handcrafted visual features for extracting features from images and using those vectors for image retrieval and classification. The authors test the WNAHVF method on two general datasets, Corel-1k and Corel-10k, and one medical dataset. The outcomes demonstrate combining Bag of Features and Local Neighbor patterns with AlexNet enhances the accuracy and gives better results in general and medical image datasets in retrieval and classification problems. This algorithm gives results that are superior to existing strategies.

Radial Moments for Image Retrieval

2017 ◽  
pp. 358-389
Author(s):  
Pooja Sharma
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Performance Measures ◽  
Similarity Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Visual Data ◽  
Image Feature Extraction ◽  
Radial Moments

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

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