NCJ Number
254594
Date Published
2019
Length
9 pages
Annotation
For convolutional neural network models that optimize an image embedding, this article proposes a method to highlight the regions of images that contribute most to pairwise similarity.
Abstract
This work is a corollary to the visualization tools developed for classification networks, but applicable to the problem domains better suited to similarity learning. The visualization shows how similarity networks that are fine-tuned learn to focus on different features. This approach is generalized to embedding networks that use different pooling strategies and provides a simple mechanism to support image similarity searches on objects or sub-regions in the query image. (publisher abstract modified)
Date Published: January 1, 2019
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