Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
This addresses computational bottlenecks for researchers and practitioners in large-scale image retrieval, though it appears incremental as it builds on existing manifold learning and dimension reduction methods.
The paper tackles the problem of high computational cost and inability to process query images in existing manifold learning methods for large-scale image retrieval by proposing an iterative manifold embedding (IME) layer learned from incomplete data, achieving over 120 times faster embedding at query time on a database of 27,000 images.
Existing manifold learning methods are not appropriate for image retrieval task, because most of them are unable to process query image and they have much additional computational cost especially for large scale database. Therefore, we propose the iterative manifold embedding (IME) layer, of which the weights are learned off-line by unsupervised strategy, to explore the intrinsic manifolds by incomplete data. On the large scale database that contains 27000 images, IME layer is more than 120 times faster than other manifold learning methods to embed the original representations at query time. We embed the original descriptors of database images which lie on manifold in a high dimensional space into manifold-based representations iteratively to generate the IME representations in off-line learning stage. According to the original descriptors and the IME representations of database images, we estimate the weights of IME layer by ridge regression. In on-line retrieval stage, we employ the IME layer to map the original representation of query image with ignorable time cost (2 milliseconds). We experiment on five public standard datasets for image retrieval. The proposed IME layer significantly outperforms related dimension reduction methods and manifold learning methods. Without post-processing, Our IME layer achieves a boost in performance of state-of-the-art image retrieval methods with post-processing on most datasets, and needs less computational cost.