Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition

Abstract

Service robots are expected to operate effectively in human-centric environments for long periods of time. In such realistic scenarios, fine-grained object categorization is as important as basic-level object categorization. We tackle this problem by proposing an open-ended object recognition approach which concurrently learns both the object categories and the local features for encoding objects. In this work, each object is represented using a set of general latent visual topics and category-specific dictionaries. The general topics encode the common patterns of all categories, while the category-specific dictionary describes the content of each category in details. The proposed approach discovers both sets of general and specific representations in an unsupervised fashion and updates them incrementally using new object views. Experimental results show that our approach yields significant improvements over the previous state-of-the-art approaches concerning scalability and object classification performance. Moreover, our approach demonstrates the capability of learning from very few training examples in a real-world setting. Regarding computation time, the best result was obtained with a Bag-of-Words method followed by a variant of the Latent Dirichlet Allocation approach.

Local-LDA: Open-Ended Learning of Latent Topics for 3D Object Recognition

“…Results showed that the proposed system supports classical learning from a batch of labelled training data and open-ended learning from online experiences of a robot. In the continuation of this work, we would like to to study how to use LDA approaches in finegrained object recognition scenarios [54] and investigate the possibility of overcoming the mentioned limitations to use deep learning approaches in open-ended domains [55].…”

The State of Lifelong Learning in Service Robots:

“…Such categories could for example be different items of cutlery, different dog, cat or other pet breeds, a variety of box shaped objects (like food container boxes, tissues, a stack of paper, etc) or different writing utensils. Attempts have been made to tackle this issue by introducing fine grained object recognition [50,83,215]. Finegrained object recognition takes into consideration small visual details of the categories that are important to distinguish them from similar categories.…”

“…Kasaei et al [94] proposed an open-ended object category learning approach just by learning specific topics per category. In another work [83], an approach is proposed to learn a set of general topics for basic-level categorization, and a category-specific dictionary for fine-grained categorization (Fig. 8).…”