A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities. This paper presents a novel approach which exploits rich mid-level convolutional features to categorize indoor scenes. Traditionally used convolutional features preserve the global spatial structure, which is a desirable property for general object recognition. However, we argue that this structuredness is not much helpful when we have large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target dataset, but it also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale dataset of 1300 object categories which are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over previous state of the art approaches on five major scene classification datasets

Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification

In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201

Indoor Localization and Mapping Using Deep Learning Networks

Over the past several decades, robots have been used extensively in environments that pose high risk to human operators and in jobs that are repetitive and monotonous. In recent years, robot autonomy has been exploited to extend their use in several non-trivial tasks such as space exploration, underwater exploration, and investigating hazardous environments. Such tasks require robots to function in unstructured environments that can change dynamically. Successful use of robots in these tasks requires them to be able to determine their precise location, obtain maps and other information about their environment, navigate autonomously, and operate intelligently in the unknown environment. The process of determining the location of the robot and generating a map of its environment has been termed in the literature as Simultaneous Localization and Mapping (SLAM). Light Detection and Ranging (LiDAR), Sound Navigation and Ranging (SONAR) sensors, and depth cameras are typically used to generate a representation of the environment during the SLAM process. However, the real-time localization and generation of map information are still challenging tasks. Therefore, there is a need for techniques to speed up the approximate localization and mapping process while using fewer computational resources. This thesis presents an alternative method based on deep learning and computer vision algorithms for generating approximate localization information for mobile robots. This approach has been investigated to obtain approximate localization information captured by monocular cameras. Approximate localization can subsequently be used to develop coarse maps where a priori information is not available. Experiments were conducted to verify the ability of the proposed technique to determine the approximate location of the robot. The approximate location of the robot was qualitatively denoted in terms of its location in a building, a floor of the building, and interior corridors. ArUco markers were used to determine the quantitative location of the robot. The use of this approximate location of the robot in determining the location of key features in the vicinity of the robot was also studied. The results of the research reported in this thesis demonstrate that low cost, low resolution techniques can be used in conjunction with deep learning techniques to obtain approximate localization of an autonomous robot. Further such approximate information can be used to determine coarse position information of key features in the vicinity. It is anticipated that this approach can be subsequently extended to develop low-resolution maps of the environment that are suitable for autonomous navigation of robots

Target Detection, Indoor Scene Classification, Visual and three-dimensional mapping for service robots in Healthcare

This thesis details work on different techniques used to implement service robots for indoor environments. This included two machine learning techniques: Target Detection and Indoor scene classification as well as two computer vision techniques: Visual mapping and three-dimensional mapping. Using these techniques, we tried to make service robots better in environments like hospitals. Assistance provided by service robots will help staff in managing tedious tasks without any problem. We used different techniques for mapping and localization so service robots can autonomously navigate from floor to floor. Depth cameras were used to make recognition and mapping better for indoor environments