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Service robots are expected to operate effectively in human-centricenvironments for long periods of time. In such realistic scenarios,fine-grained object categorization is as important as basic-level objectcategorization. We tackle this problem by proposing an open-ended objectrecognition approach which concurrently learns both the object categories andthe local features for encoding objects. In this work, each object isrepresented using a set of general latent visual topics and category-specificdictionaries. The general topics encode the common patterns of all categories,while the category-specific dictionary describes the content of each categoryin details. The proposed approach discovers both sets of general and specificrepresentations in an unsupervised fashion and updates them incrementally usingnew object views. Experimental results show that our approach yieldssignificant improvements over the previous state-of-the-art approachesconcerning scalability and object classification performance. Moreover, ourapproach demonstrates the capability of learning from very few trainingexamples in a real-world setting. Regarding computation time, the best resultwas obtained with a Bag-of-Words method followed by a variant of the LatentDirichlet Allocation approach.

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