Gleo-Det: Deep Convolution Feature-Guided Detector with Local Entropy Optimization for Salient Points
This work addresses limitations in unsupervised feature detection for computer vision applications, but it appears incremental as it builds on existing ideas.
The paper tackles the problem of unsupervised feature detection for image matching by combining repeatability constraints with deep convolution feature guidance and using entropy-based cost functions to avoid training issues. Experiments show the method achieves competitive results compared to state-of-the-art approaches.
Feature detection is an important procedure for image matching, where unsupervised feature detection methods are the detection approaches that have been mostly studied recently, including the ones that are based on repeatability requirement to define loss functions, and the ones that attempt to use descriptor matching to drive the optimization of the pipelines. For the former type, mean square error (MSE) is usually used which cannot provide strong constraint for training and can make the model easy to be stuck into the collapsed solution. For the later one, due to the down sampling operation and the expansion of receptive fields, the details can be lost for local descriptors can be lost, making the constraint not fine enough. Considering the issues above, we propose to combine both ideas, which including three aspects. 1) We propose to achieve fine constraint based on the requirement of repeatability while coarse constraint with guidance of deep convolution features. 2) To address the issue that optimization with MSE is limited, entropy-based cost function is utilized, both soft cross-entropy and self-information. 3) With the guidance of convolution features, we define the cost function from both positive and negative sides. Finally, we study the effect of each modification proposed and experiments demonstrate that our method achieves competitive results over the state-of-the-art approaches.