Deep Feature Factorization For Content-Based Image Retrieval And Localization

State of the art content-based image retrieval algorithms owe their excellent performance to the rich semantics encoded in the deep activations of a convolutional neural network. The difference between these algorithms lies mostly in how activations are combined into a compact global image descriptor. In this paper, we propose to use deep feature factorization to achieve this goal. By factorizing CNN activations, we decompose an input image into semantic regions, represented by both spatial saliency heatmaps and basis vectors serving as descriptors for those regions. When combined to form a global image descriptor, our experiments show that DFF surpasses the state of the art in both image retrieval and localization of the region of interest within the set of retrieved images

Deep Semantic Segmentation Using Nir As Extra Physical Information

Deep neural networks for semantic segmentation are most often trained with RGB color images, which encode the radiation visible to the human eyes. In this paper, we study if additional physical scene information, specifically Near-Infrared (NIR) images, improve the performance of neural networks. NIR information can be captured with conventional silicon-based cameras and provide complementary information to visible images regarding object boundaries and materials. In addition, extending the networks' input from a three to a four channel layer is trivial with respect to changes to the architecture and additional parameters. We perform experiments on several state-of-the-art neural networks trained both on RGB alone and on RGB plus NIR and show that the additional image channel consistently improves semantic segmentation accuracy over conventional RGB input even for powerful architectures

Spherical Sampling and Color Transformations

In this paper, we present a spherical sampling technique that can be employed to find optimal sensors for trichromatic color applications. The advantage over other optimization techniques is that it assures a global minimum is found, and that not only one, but a set of solutions is retained if so desired. The sampling technique is used to find all possible RGB sensors that exhibit favorable chromatic adaptation transform (CAT) behavior when tested on Lam’s corresponding color data set, subject to a CIE Delta E94 error criterion. We found that there are a number of sensors that meet the criterion, and that the Bradford, Sharp, and CMCCAT2000 sensors are not unique