The article discusses the challenge of distinguishing between similar dog breeds, highlighting the author's personal experience while developing PawMatchAI. It explores how professional veterinarians rely on a multi-level feature analysis for accurate dog breed identification, focusing on various attributes such as body proportions and fur texture. This insight led the author to reconsider traditional CNN approaches, which entangle features within millions of parameters. To address this, the author created the Morphological Feature Extractor, designed to enable AI to understand dog breeds in a structured, expert-like manner.
I realized that when I recognize objects, I don't process all details at once. Instead, I first notice the overall shape, then refine my focus on specific features.
Experts don't just memorize images, they analyze structured traits such as overall body proportions, head features, fur texture and distribution, and more.
While traditional CNNs are powerful, they don't explicitly separate key characteristics the way human experts do, which limits their interpretability.
I designed the Morphological Feature Extractor, an approach that helps AI analyze breeds in structured layers, similar to how experts do.
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