Image Retrieval Based on GA Integrated Color Vector Quantization and Curvelet Transform

2012 ◽  
pp. 406-413
Author(s):  
Yungang Zhang ◽  
Tianwei Xu ◽  
Wei Gao
Keyword(s):  
Image Retrieval ◽  
Vector Quantization ◽  
Curvelet Transform ◽  
Color Vector

New Hardware Architecture for Self-Organizing Map Used for Color Vector Quantization

2019 ◽  
Vol 29 (01) ◽  
pp. 2050002
Author(s):  
Khaled Ben Khalifa ◽  
Ahmed Ghazi Blaiech ◽  
Mehdi Abadi ◽  
Mohamed Hedi Bedoui
Keyword(s):  
Vector Quantization ◽  
Recent Literature ◽  
Network Architectures ◽  
Self Organizing Map ◽  
Description Language ◽  
Field Programmable ◽  
Color Vector ◽  
Hardware Description ◽  
Som Network ◽  
Self Organizing

In this paper, we present a new generic architectural approach of a Self-Organizing Map (SOM). The proposed architecture, called the Diagonal-SOM (D-SOM), is described as an Hardware–Description-Language as an intellectual property kernel with easily adjustable parameters.The D-SOM architecture is based on a generic formalism that exploits two levels of the nested parallelism of neurons and connections. This solution is therefore considered as a system based on the cooperation of a distributed set of independent computations. The organization and structure of these calculations process an oriented data flow in order to find a better treatment distribution between different neuroprocessors. To validate the D-SOM architecture, we evaluate the performance of several SOM network architectures after their integration on a Xilinx Virtex-7 Field Programmable Gate Array support. The proposed solution allows the easy adaptation of learning to a large number of SOM topologies without any considerable design effort. [Formula: see text] SOM hardware is validated through FPGA implementation, where temporal performance is almost twice as fast as that obtained in the recent literature. The suggested D-SOM architecture is also validated through simulation on variable-sized SOM networks applied to color vector quantization.

RETRIEVING IMAGES BY COMPARING HOMOGENEOUS COLOR AND TEXTURE OBJECTS IN THE IMAGE

2004 ◽  
Vol 18 (06) ◽  
pp. 1093-1110 ◽  
Author(s):  
HUN-WOO YOO ◽  
DONG-SIK JANG ◽  
KWANG-KYU SEO ◽  
MYUNG-EUI LEE
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Vector Quantization ◽  
Textural Features ◽  
Clustering Method ◽  
Retrieval Method ◽  
Simple Representation ◽  
Object Based ◽  
Efficient Retrieval ◽  
Image Segmentation Algorithm

An object-based image retrieval method is addressed in this paper. For that purpose, a new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and textural features are extracted from each pixel in the image and these features are used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terms of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In the retrieval case, two comparing schemes are proposed. Comparisons between one query object and multi-objects of a database image and comparisons between multi-query objects and multi-objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into the database.

Sign in / Sign up

Export Citation Format

Share Document