# Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks
## 1 Introduction
In [13] an algorithm meant to aid VI people was proposed to read texts encountered in natural scenes. Door detection in unfamiliar environments was also considered in [14]. Assisted indoor scene understanding for single object detection and recognition was also suggested in [15] by means of the scale invariant feature transform. In [16], the authors proposed a system called Blavigator to assist VI, which was composed of an interface, Geographic Information System (GIS), navigation, object recognition and a central decision module. In a known location, this system uses object recognition algorithms to provide contextual feedback to the user in order to validate the positioning module and the navigation system for the VI. In another work, [17] the authors proposed a vision-based wayfinding aid for blind people to access unfamiliar indoor environments to find different rooms such as office, and laboratory and building amenities such as exits and elevators. This system incorporates object detection with text recognition. The recognition part of VI assistance technology is mostly approached from a single object perspective (i.e., single-label classification where the attention is focused on single objects). This approach, despite its usefulness in carrying out information to the VI person, remains rather limited since multiple objects may be encountered in daily life. Thereupon, broadening the emphasis to multiple objects by solving the problem from a multi-label classification perspective seems to offer a more significant alternative in terms of usefulness to the VI user. The concept of multi-label classification was adopted recently in some works based on handcrafted features, and was commonly termed 'coarse' scene multi-labeling/description [18; 19]. It has demonstrated its effectiveness in detecting the presence of multiple objects of interest across an indoor environment in a very low processing time. Similar designs were envisioned in other areas such as remote sensing [20] to address the issue of object detection in high-resolution images. Recently deep convolutional neural networks (CNNs) have achieved impressive results in applications such as image classification [21; 22; 23; 24; 25], object detection [26; 27; 28; 29], and image segmentation [30; 31]. These networks have the ability to learn richer representations in a hierarchical way compared to handcrafted-based methods. Modern CNNs are made up of several alternating convolution blocks with repeated structures. The whole architecture is trained end-to-end using the backpropagation algorithm [32]. Usually, CNNs perform well for datasets with large labeled data. However, they are prone to overfitting when dealing with datasets with very limited labeled data as in the context of our work. In this case, it is has been shown in many studies that it is more appealing to transfer knowledge from CNNs (such as AlexNet [25], VGG-VD [33], GoogLeNet [24], and ResNet [23]) pre-trained on an auxiliary recognition task with very large labeled data instead of training from scratch [34; 35; 36; 37]. The possible knowledge transfer solutions include fine-tuning of the labeled data of the target dataset or to exploit the network feature representations with an external classifier. We refer the readers to [35] where the authors presents several factors affecting the transferability of these representations. In this paper, we propose an alternative solution suitable for datasets with limited training samples mainly based on convolutional SVM networks (CSVMs). Actually, SVMs are among the most popular supervised classifiers available in the literature. They rely on the margin maximization principle, which makes them less sensitive to overfitting problems. They have been widely used for solving various recognition problems. Additionally, they are also commonly placed on the top of a CNN feature extractor for carrying out the classification task [35].