Unsupervised Learning Framework of Interest Point Via Properties Optimization
This addresses the need for flexible and efficient interest point detection in computer vision, offering an unsupervised approach that adapts to diverse scenes.
The paper tackles the problem of unsupervised interest point detection and description by jointly learning detector and descriptor through a framework optimizing sparsity, repeatability, and discriminability. It results in a Property Network (PN) that outperforms state-of-the-art methods on image matching benchmarks without retraining.
This paper presents an entirely unsupervised interest point training framework by jointly learning detector and descriptor, which takes an image as input and outputs a probability and a description for every image point. The objective of the training framework is formulated as joint probability distribution of the properties of the extracted points. The essential properties are selected as sparsity, repeatability and discriminability which are formulated by the probabilities. To maximize the objective efficiently, latent variable is introduced to represent the probability of that a point satisfies the required properties. Therefore, original maximization can be optimized with Expectation Maximization algorithm (EM). Considering high computation cost of EM on large scale image set, we implement the optimization process with an efficient strategy as Mini-Batch approximation of EM (MBEM). In the experiments both detector and descriptor are instantiated with fully convolutional network which is named as Property Network (PN). The experiments demonstrate that PN outperforms state-of-the-art methods on a number of image matching benchmarks without need of retraining. PN also reveals that the proposed training framework has high flexibility to adapt to diverse types of scenes.