MATLAB PROJECT
Class Agnostic Image
Common Object Detection
Abstract:
Learning similarity of two images is an
important problem in computer vision and has many potential applications. Most
of the previous works focus on generating image similarities in three aspects:
global feature distance computing, local feature matching, and image concepts
comparison. However, the task of directly detecting the class agnostic common
objects from two images has not been studied before, which goes one step
further to capture image similarities at the region level. In this paper, we
propose an end-to-end image Common Object Detection Network (CODN) to detect
class agnostic common objects from two images. The proposed method consists of
two main modules: locating module and matching module. The locating module
generates candidate proposals of each two images. The matching module learns
the similarities of the candidate proposal pairs from two images, and refines
the bounding boxes of the candidate proposals. The learning procedure of CODN
is implemented in an integrated way and a multi-task loss is designed to
guarantee both region localization and common object matching. Experiments are
conducted on PASCAL VOC 2007 and COCO 2014 datasets. The experimental results
validate the effectiveness of the proposed method.
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