2017/09/05 · We argue that non-metric similarity functions based on neural networks can build a better model of human visual perception than standard metric distances.

Instead of using a metric distance function, we propose to train a neural network model to learn a similarity score between a pair of visual representations.

We argue that the visual human perception is not properly explained by linear metrics and thus, non-metric visual similarity may obtain better performance than ...

Matlab code for the paper "Learning Non-Metric Visual Similarity for Image Retrieval", in which a similarity network is proposed to estimate a visual ...

In this paper, we propose to learn a non-metric visual similarity function directly from image representations to measure how alike two images are. Experiments ...

1 year master's course work. Contribute to peternara/Histogram-Loss-Learning-Non-Metric-Visual-Similarity-for-Image-Retrieval development by creating an ...

Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by ...

2019/02/08 · In this work, we propose a model to learn a non-metric visual similarity function on top of image representations for pushing ac- curacy in ...

In this paper, we present a new similarity measure reflecting the nonlinearity of human perception. Based on this measure, we develop a similarity ranking ...

2023/05/05 · SUMMARY. Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local con-.