Recognizing unknown objects with attributes relationship model. Issue 23 (15th December 2015)

• Record Type: Journal Article
• Authors: Hoo, Wai Lam; Chan, Chee Seng
• Is Part Of: Expert systems with applications. Volume 42:Issue 23(2015)
• Page Start: 9279
• Page End: 9283
• Publication Date: 2015-12-15
• Subjects: Object recognition -- Zero-shot learning -- Attributes -- Image hierarchy; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.07.049
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: This paper tackle zero-shot learning problem in object recognition domain. Unknown objects that have no training images are related with known objects. A model that combines the benefits of attributes and image hierarchy is proposed. The proposed method achieves state-of-the-art accuracy in AwA dataset. Abstract: Generally, training images are essential for a computer vision model to classify specific object class accurately. Unfortunately, there exist countless number of different object classes in real world, and it is almost impossible for a computer vision model to obtain a complete training images for each of the different object class. To overcome this problem, zero-shot learning algorithm was emerged to learn unknown object classes from a set of known object classes information. Among these methods, attributes and image hierarchy are the widely used methods. In this paper, we combine both the strength of attributes and image hierarchy by proposing Attributes Relationship Model (ARM) to perform zero-shot learning. We tested the efficiency of the proposed algorithm on Animals with Attributes (AwA) dataset and manage to achieve state-of-the-art accuracy (50.61%) compare to other recent methods.