Mixture of Deep CNN-based Ensemble Model for Image Retrieval

• Main Authors: Hsin-Kai Huang, 黃信凱
• Other Authors: Pao-Chi Chang
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/42331837138318267488

碩士 === 國立中央大學 === 通訊工程學系 === 104 === Rapid Internet deployment and technology development have led us into the era of Big Data. There are numerous digital image data being continuously produced by our pads, smartphones, digital cameras, and other portable multimedia devices. We are facing many problems of challenge. One of the primary problems is how we can find an effective method to manage our image datasets and conduct customized retrieval. We propose a model, which combines two distinguishable deep Convolutional Neural Networks (CNN) architectures to achieve better performance for image retrieval. This paper proposes an ensemble model based on a mixed architecture of deep CNN. It utilizes two kinds of deep learning networks, AlexNet and Network In Network (NIN), to obtain the image features, and to compute the weighted average feature vectors for image retrieval. From our experiment result, ensemble architecture could effectively enhance learning with higher accuracy than single CNN in image classification. The proposed Mixture of deep CNN-based Ensemble Model (MCNNE) was applied to CIFAR-10 and CIFAR-100 datasets. It achieved 0.867 and 0.526 Mean Average Precision (MAP) in image retrieval tasks, respectively.