Wavelet transforms and neural networks applied to image retrieval

Author(s):  
A.C. Gonzalez ◽  
J.H. Sossa ◽  
Edgardo.M. Felipe ◽  
O. Pogrebnyak
2018 ◽  
Vol 7 (3.1) ◽  
pp. 13
Author(s):  
Raveendra K ◽  
R Vinoth Kanna

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.  


2008 ◽  
Author(s):  
Alireza Akhbardeh ◽  
Meltem Izzetoglu ◽  
Scott Bunce ◽  
Kambiz Pourrezaei ◽  
Banu Onaral

Author(s):  
Siddhivinayak Kulkarni

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.


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