A novel large-scale multimedia image data classification algorithm based on mapping assisted deep neural network

A novel large-scale multimedia image data classification algorithm based on mapping assisted deep... With the increasing number of the images, how to effectively manage and use these images becomes an urgent problem to be solved. The classification of the images is one of the effective ways to manage and retrieve images. In this paper, we propose a novel large-scale multimedia image data classification algorithm based on deep learning. We firstly select the image characteristics to represent the flag for retrieval, which represents the color, texture and shape characteristics respectively. A feature of color is the most basic image data, mainly including the average brightness, color histogram and dominant color, etc. What the texture refers to is the image data in the anomalous, macroscopic as well as orderly one key character that on partial has. The contour feature extraction of image data needs to rely on the edge detection, edge of the detected edge through the connection or grouping to form a meaningful image event. Secondly, we revise the convolutional neural network model based on the pooling operation optimization, the pooling is in the process of the convolution operation to extract the image characteristics of the different locations to gather statistics. Furthermore, we integrate the parallel and could storage strategy to enhance the efficiency of the proposed methodology. The performance of the algorithm is verified, compared with the other state-of-the-art approaches, the proposed one obtains the better efficiency and accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

A novel large-scale multimedia image data classification algorithm based on mapping assisted deep neural network

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Springer US
Copyright © 2017 by Springer Science+Business Media New York
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
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