Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition
Most robots lack the ability to learn new objects from past experiences. To migrate a robot to a new environment one must often completely re-generate the knowledgebase that it is running with. Since in open-ended domains the set of categories to be learned is not predefined, it is not feasible to assume that one can pre-program all object categories required by robots. Therefore, autonomous robots must have the ability to continuously execute learning and recognition in a concurrent and interleaved fashion. This paper proposes an open-ended 3D object recognition system which concurrently learns both the object categories and the statistical features for encoding objects. In particular, we propose an extension of Latent Dirichlet Allocation to learn structural semantic features (i.e. topics) from low-level feature co-occurrences for each category independently. Moreover, topics in each category are discovered in an unsupervised fashion and are updated incrementally using new object views. The approach contains similarities with the organization of the visual cortex and builds a hierarchy of increasingly sophisticated representations. Results show the fulfilling performance of this approach on different types of objects. Moreover, this system demonstrates the capability of learning from few training examples and competes with state-of-the-art systems.
In the “Introduction” section, the authors point out the neurophysiological evidences that a human brain has a hierarchical structure for the object recognition, and that the learning and recognition of objects occurs concurrently in a human brain. Then, they briefly explained how they built their 3D object recognition model that concurrently learns and recognizes the objects that works in the similar way to the human brain.  The authors talk about conventional approaches for the object recognition problem in the “Related work” section. They mention about the probabilistic latent semantic indexing (pLSI), latent Dirichlet allocation (LDA) and its variations, etc. And they point out that none of these conventional models can learn and recognize objects in an open-ended manner. It was explained in the section, that an open-ended system is a system that can concurrently learn and recognize objects. They also mentioned that the number of classes for an open-ended system is not predefined and can grow continuously.  For section 3 to 6, the authors explained their proposed model. The model is designed to use the LDA in recognizing as well as in learning both categories and their features in an open-ended manner. To do this, they used 5 layers to build their system: feature layer, Bag-of-Words (BoW) layer, topic layer, view layer, category layer. The feature layer computes key points from spin-images. Then the BoW layer assigns a visual word for each spin-images. The topic layer uses LDA to discover abstract topics. Then the view layer stores the random mixture over latent topics of categories. Finally, the categorical output is made in the category layer.  The authors show their 3 experimental results at section 7. Their model was tested with a dataset using 10-fold cross validation method. The results showed that their topic layer, which develops independent topics for each category, showed better performance than the conventional approach (supervised LDA, class LDA) of sharing topics. And also showed that the test accuracy obtained from BoW layer is higher than the feature layer. Second experiment was conducted in an open-ended manner and showed a good performance of the model in recognizing and learning object categories concurrently. Finally, in the last experiment, the authors demonstrated how the model can work for robots with the aid of human instructors. 
For the first experiment, the authors should have compared their model with a variety of models. There are many deep learning models for 3D object recognition, yet, their performances were not compared with that of the authors’ model when their model was tested in an off-line condition. Therefore I evaluated the technical quality of this paper as “Poster level”. In the paper, the authors used abbreviations such as PCL, OCD without explaining what they stand for. Therefore I gave “Poster level” for the technical quality. The proposed model has novelty in that it recognizes and learns the category of 3D objects concurrently using latent Dirichlet allocation. By using the model, the tedious work of making dataset, and training a robot with it to adapt to its working environment may be alleviated for the researchers in the robotics field. Therefore, I believe that this study has made an advance in the field of vison processing for the robotics. Therefore I evaluated both novelty and potential usefulness as “Oral level”.