2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | ARS-6.8 | ||
| Paper Title | LEARNING AN ADAPTATION FUNCTION TO ASSESS IMAGE VISUAL SIMILARITIES | ||
| Authors | Olivier Risser-Maroix, Camille Kurtz, Nicolas Lomenie, Université de Paris, France | ||
| Session | ARS-6: Image and Video Interpretation and Understanding 1 | ||
| Location | Area H | ||
| Session Time: | Tuesday, 21 September, 15:30 - 17:00 | ||
| Presentation Time: | Tuesday, 21 September, 15:30 - 17:00 | ||
| Presentation | Poster | ||
| Topic | Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Human perception is routinely assessing the similarity between images, both for decision making and creative thinking. But the underlying cognitive process is not really well understood yet, hence difficult to be mimicked by computer vision systems. State-of-the-art approaches using deep architectures are often based on the comparison of images described as feature vectors learned for image categorization task. As a consequence, such features are powerful to compare semantically related images but not really efficient to compare images visually similar but semantically unrelated. Inspired by previous works on neural features adaptation to psycho-cognitive representations, we focus here on the specific task of learning visual image similarities when analogy matters. We propose to use different layers of a categorization-based CNN (pre-trained on ImageNet) as a rough approximation of the visual cortex and learn only an adaptation function corresponding to the approximation of the the primate IT cortex through the metric learning framework. Our experiments conducted on the Totally Looks Like image dataset highlight the interest of our method, by increasing the retrieval scores @5 by 1.75×. | ||