Knowledge Based Unsupervised Object Discovery Using Probabilistic Randomized Hough Transform (PRHT) With Deep Learning Classification (DLC) - Johny, Mereena; Haldurai, L (International Journal of Computer Sciences and Engineering, 2018)
Latent topics models have become a popular paradigm in many computer vision applications due to their capability to discover semantics in visual content. Various knowledge based object discovery algorithms for the classification problem in dependent images are appearing in the literature. However, these algorithms mostly suffer from the following two problems: image metadata and time measures. To overcome this kind of problem, this paper presents a Probabilistic Randomized Hough Transform (PRHT) with Deep Learning Classification Algorithm (DLC) algorithm performs the object discovery and localization used by deep learning classification algorithm. The proposed method of object regions are efficiently matched across images using a Probabilistic Randomized Hough Transform with Deep Learning Classification that evaluates the confidence for each candidate correspondence considering both appearance and spatial consistency. The achieved PRHT-DLC has high accuracy and performance increases compared to the previous method of Pipeline method and Latent Dirichlet allocation (LDA) algorithms.