{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:30:33Z","timestamp":1783639833146,"version":"3.55.0"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>The volume of images uploaded to the internet is increasing at an unprecedented rate, making image deduplication, through accurate near-duplicate detection, a critical task in computer vision. However, comparing images for similarity remains challenging due to complex visual structures and subtle appearance variations.We propose a novel embedding method for image similarity detection. It constructs an enriched representation by concatenating outputs from multiple intermediate layers of a pre-trained ResNet50 convolutional neural network and trains a lightweight decision network on top to classify image pairs. Unlike aggregation approaches that average or sum intermediate features, our method preserves both low-level and high-level information in a single descriptor and maintains feature diversity. The multi-level embedding is further normalized to balance feature contributions and is evaluated against classical keypoint descriptors, a DCT-based perceptual hash, and a standard single-layer ResNet50 embedding.We evaluate this method on three real-world image deduplication tasks derived from real estate listings, covering (a) near-identical property photos with graphical overlays, (b) interior room photographs captured from different angles, and (c) schematic floor plan images. The proposed embedding achieves F1-scores of 0.96, 0.87, and 0.77, representing a 10-15% absolute improvement over baseline methods, including classical feature descriptors and standard ResNet50 final-layer embeddings.This approach has been successfully deployed in production on a large-scale real estate platform, reducing duplicate images and improving search quality. The results demonstrate that multi-layer CNN embeddings with explicit feature preservation offer a robust and scalable solution for near-duplicate image detection in structured domains such as real estate photography and schematic floor plans.<\/jats:p>","DOI":"10.31449\/inf.v50i9.12111","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:13Z","timestamp":1773354073000},"source":"Crossref","is-referenced-by-count":1,"title":["Multi-Level CNN Feature Fusion from ResNet50 for Near-Duplicate Image Detection in Real Estate Imagery"],"prefix":"10.31449","volume":"50","author":[{"given":"Taras","family":"Panchenko","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Artem","family":"Bozhok","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Volodymyr","family":"Kubytskyi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,12]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12111\/6556","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12111\/6556","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:14Z","timestamp":1773354074000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i9.12111","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}