This paper presents a novel framework for online incremental place recognition in an indoor environment. The framework addresses the scenario in which scene images are gradually obtained during long-term operation in the real-world indoor environment. Multiple users may interact with the classification system and confirm either current or past prediction results; the system then immediately updates itself to improve the classification system. This framework is based on the proposed \emph{n}-value self-organizing and incremental neural network (\emph{n}-SOINN), which has been derived by modifying the original SOINN to be appropriate for use in scene recognition. The evaluation was performed on the standard MIT 67-category indoor scene dataset and shows that the proposed framework achieves the same accuracy as that of the state-of-the-art offline method, while the computation time of the proposed framework is significantly faster and fully incremental update is allowed. Additionally, a small extra set of training samples is incrementally given to the system to simulate the incremental learning situation. The result shows that the proposed framework can leverage such additional samples and achieve the state-of-the-art result.

A novel method that automatically counts number of objects piled or stacked to each other in mobile device image is proposed. Distinct from other methods, the proposed method automatically determines the size and shape and detects the template of unknown to-be-counted objects. Such template is then used to perform fast template matching all over the input image to count the number of the same objects. The similarity function for determining the detected object has been proposed. The evaluation on speed and accuracy was performed by testing on images of various construction materials captured from on real site by the Android smart phone. The result suggests that the proposed method outperforms all baselines including the recent lattice detection based approach. The computation time is sufficiently fast for real-time use in mobile devices. It can also efficiently deal with the problem of information loss (i.e., lost edges) due to various unpredicted lighting condition. In term of time, the comparison of counting time was also done between the method and human.

The paper presents a new online incremental zero-shot learning method for applications in robotics and mobile communications where attribute labeling is obtained via online interaction with users, and where the potential for inconsistency exists. Unique to most previous offline batch learning methods, the proposed method is based on the indirect-attribute-prediction (IAP) model instead of the direct-attribute-prediction (DAP). Using self-organizing and incremental neural networks (SOINN) as the learning mechanism, our method can learn new attributes and update existing attributes in an online incremental manner while retaining as high accuracy as that of the state-of-the-art offline method. Compared to the offline methods, the computation time has also been reduced by more than 99%. Two experiments evaluated two aspects of the proposed method. First, our method clearly outperforms the previous IAP-based offline method in terms of both time and accuracy, and yield approximately the same accuracy as the DAP-based offline method. Second, the proposed method can deal with situations where object attributes are gradually labeled via interaction with many users and where some of them may be incorrect. This scenario is very important for applications in mobile communications and robotics where some objects and attributes may be initially unknown and must be learnt online.

A new approach of unseen place classification in a commercial district is presented. It can classify input scenes into the correct place classes without the needs for sample images of places for training. The number of place classes and their definition are supervised by humans using text information only. A description of individual place classes is obtained from humans as a set of words that are regarded as the disjoint-text-attributes of the unseen place. During classification, our approach determines the number of text-attributes found in an image. Our approach runs in an online incremental manner in the sense that the description of place classes can be updated and a new place class can be added at any time. Our approach can be used, does not require any training dataset, and is available in multiple languages. The evaluation is done by a set of Google Street View images of a shopping area in Japan where both the Japanese and English languages are available. The result shows that the proposed method outperforms the state-of-the-art methods of scene text recognition and standard pattern recognition. The computation is sufficiently fast for real-time application.

Classifying new unseen object classes has become a popular topic of research in the computer-vision and robotics community. Coping with this problem requires determining the attributes shared among objects and transferring them for use in classifying unseen object classes. Nevertheless, most current state-of-the-art methods require a fully offline training process and take a very long time for the batch training process, which renders them inapplicable for use in online applications such as robotics. This study proposes a novel online and incremental approach for learning and transferring the learned attributes in order to classify another disjoint set of image classes. Among three methods proposed in this paper, a method combining those favorable features of a self-organizing incremental neural network (SOINN) and a support vector machine (SVM) achieves the best performance. This method, called the Alt-SOINN-SVM, can run online incrementally, similar to an SOINN, and perform accurate classification, similar to an SVM. An evaluation was performed with 50 classes of an animal with an attributes dataset (>;30,000 images). The results shows that despite the great reduction in both learning time (92.25% reduction) and classification time (99.87% reduction), and possessing the ability for incremental learning on gradually obtained samples, the proposed method offers reasonably good accuracy for classification. Furthermore, the proposed methods are applicable to use with the increasing number of attribute which improves the accuracy gradually and incrementally.

This paper presents a fast, online, and incremental solution for an appearance-based loop closure detection problem in an indoor environment. This problem is important in terms of the navigation of mobile robots. Appearance-based Simultaneous Localization And Mapping (SLAM) for a highly dynamic environment, called Position Invariant Robust Feature Navigation (PIRF-Nav), was first proposed by Kawewong et al. in 2010. Their results showed major improvements from other state-of-the-art methods. However, the computational expense of PIRF-Nav is beyond real time, and it consumes a tremendous amount of memory. These two factors hinder the use of PIRF-Nav for mobile robot applications. This study proposed (i) modified PIRF extraction that makes the system more suitable for an indoor environment and (ii) new dictionary management that can eliminate redundant searching and conserve memory consumption. According to the results, our proposed method can finish tasks up to 12 times faster than PIRF-Nav, with only slight percentage decline in a recall, while the precision remains 1. In addition, for a more challenging task, we collected additional data from a crowded university canteen during lunch time. Even in this cluttered environment, our proposed method performs better with real-time processing compared with other methods.

A new approach for vision-based navigation in mobile robots is presented. Instead of incremental spectral clustering (ISC), which is considered state-of-the-art, the method of self-organizing incremental neural networks (SOINN) is used for visual space clustering. Using SOINN, the number of nodes in the topological map matches well with the environment. The time used for incremental map building is markedly less than that used for ISC. Furthermore, the rate of single image classification is higher than that of ISC.

We introduced a new approach to classify 3D texture images based on output selection from two classification methods. For a set of real-world textured images, we found that, using classification based only on texture information, some images obtained under unknown conditions appeared to have similar surfaces and could not be recognized correctly. Another classification method, based on color information, was therefore performed in parallel to solve such a problem. Some knowledge is required to efficiently select outputs from texture-based classification (TBC) and color-based classification (CBC). Reliability tables (RT), created in the pre-testing stage (PTS) of system training, are proposed to obtain that knowledge. Performance is evaluated by employing over 5600 texture images corresponding to 61 real-world surface samples extracted from the Columbia-Utrecht reflectance and texture (CUReT) database K.J. Dana et al., 1999. Our experiments produce better classification results than those in M. Varma and A. Zisserman (2005), which are the best results available to date.