Image retrieval method based on CNN and dimension reduction
This work addresses image retrieval challenges for e-commerce applications, but it is incremental as it combines existing techniques like CNN and dimension reduction.
The paper tackled the problem of high-dimensional correlated features in image retrieval by using a CNN for feature extraction and multilinear principal component analysis for dimension reduction, followed by binary hash coding, resulting in better retrieval performance on e-commerce image datasets compared to PCA-based methods.
An image retrieval method based on convolution neural network and dimension reduction is proposed in this paper. Convolution neural network is used to extract high-level features of images, and to solve the problem that the extracted feature dimensions are too high and have strong correlation, multilinear principal component analysis is used to reduce the dimension of features. The features after dimension reduction are binary hash coded for fast image retrieval. Experiments show that the method proposed in this paper has better retrieval effect than the retrieval method based on principal component analysis on the e-commerce image datasets.