Context-aware CLIP for Enhanced Food Recognition

Year 2025, Volume: 5 Issue: 1, 7 - 13, 16.06.2025

Övgü Öztürk Ergün

https://doi.org/10.54569/aair.1707867

Abstract

Generalization of food image recognition frameworks is difficult due to the wide variety of food categories in cuisines across cultures. The performance of the deep neural network models highly depends on the training dataset. To overcome this problem, we propose to extract context information from images in order to increase the discrimination capacity of networks. In this work, we utilize the CLIP architecture with the automatically derived ingredient context from food images. A list of ingredients are associated with each food category, which is later modeled as text after a voting process and fed to a CLIP architecture together with input image. Experimental results on the Food101 dataset show that this approach significantly improves the model’s performance, achieving a 2% overall increase in accuracy. This improvement varies across food classes, with increases ranging from 0.5% to as much as 22%. The proposed framework, CLIP fed with ingredient text, outperforms Yolov8 (81.46%) with 81.80% top 1 overall accuracy over 101 classes.

Keywords

Food Image Processing , CLIP , Ingredient Analysis , Context , Deep Learning , AI

Yemek Kategori Tanıma için İçerik Farkındalıklı CLIP

Abstract

Gıda görüntü tanıma çözümlerinin genelleştirilmesi, kültürler arası mutfaklardaki gıda kategorilerinin çok çeşitli olması nedeniyle zordur. Derin sinir ağı modellerinin performansı büyük ölçüde eğitim veri kümesine bağlıdır. Bu sorunun üstesinden gelmek için, ağların sınıfları daha iyi ayırt edebilme kapasitesini artırmak amacıyla görüntülerden bağlam bilgisi çıkarmayı öneriyoruz. Bu çalışmada, gıda görüntülerinden otomatik olarak türetilen bileşen bağlamına sahip CLIP mimarisini kullanıyoruz. Her bir gıda kategorisiyle bir bileşen listesi ilişkilendirilir ve daha sonra bir oylama sürecinden sonra metin olarak modellenir ve giriş görüntüsüyle birlikte bir CLIP mimarisine beslenir. Food101 veri kümesindeki deneysel sonuçlar, bu yaklaşımın modelin performansını önemli ölçüde iyileştirdiğini ve doğrulukta %2'lik bir genel artış sağladığını göstermektedir. Bu iyileştirme, %0,5'ten %22'ye kadar değişen artışlarla gıda sınıflarına göre değişmektedir. Önerilen bileşen metniyle beslenen CLIP yöntemi, 101 sınıf üzerinde %81,80'lik ilk 1 genel doğrulukla Yolov8'i (%81,46) geride bırakmaktadır.

Keywords

Yemek Resim Tanıma , CLIP , Derin Öğrenme , YZ , İçerik , Malzeme Analizi

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