Automatic Identification of Local Features Representing Image Content with the Use of Convolutional Neural Networks  
[PERSON]  
Institute of Information Technology, Lodz University of Technology, ul. Wolczanska 215, 90-924 Lodz, Poland; [EMAIL_ADDRESS] (P.T. ); [EMAIL_ADDRESS] (B.S.) [PERSON]  
Correspondence: [EMAIL_ADDRESS]; Tel. : +48-42-631-39-57  
[PERSON]  
Theoretical  
implementation and there are trainable models that can operate on raw pixels), they are usually combined together. The need for this combination originates in the limitations of existing models (e.g., specific input format) as well as in the necessity of selection of optimal model parameters (even carefully designed algorithms require problem specific fine-tuning). An algorithm solving a given problem, created both with the use of machine learning techniques and without them, requires domain knowledge either to encode it in a computer program or to design the training objective. Unfortunately, domain experts usually have problems with sharing their knowledge in a form of ready-to-use mathematical formulas. They prefer to express it in imprecise natural language (it must be somehow adapted to become applicable in the source code of the program) or they provide it in a form of a training set (for a given input they specify the expected output). In both cases, it may be easier for them to operate on simplified representations rather than on millions of pixels. Training set preparation may be especially troublesome for some specific tasks. In particular, when precise image segmentation is the goal of analysis, the knowledge acquisition at pixel level can be tiresome and time-consuming. Moreover, if it concerns, e.g., medical applications, where the number of experts is limited, acquiring a huge and representative set of examples, required by most of machine learning algorithms, becomes almost impossible. Another important aspect of image representation choice is interpretability of the algorithm results. Nowadays many artificial intelligence techniques have become a part of our life. Some of them are, or in the near future, will become responsible for our health and even life. Consequently, their authors must be prepared to at least explain their general principles to potential consumers to convince them that their product is safe. Operating on individual pixels makes it practically impossible. If instead the applied representation is significantly reduced and its components can be assigned a meaningful interpretation, such an explanation becomes plausible. To conclude, the search for alternative image representations constitutes an important task from image analysis point of view. Moreover, it is very interesting itself as it may also allow better understanding of the principles of human visual system operation. There are many evidences that this system also tries to organize the recognition process creating several intermediate representations corresponding to elements of the observed scene [1]. Such intermediate representations are also observed in trained convolutional neural networks (CNN) [2; 3; 4; 5; 6] as they try to imitate the activity of visual cortex (to some extent). This was the main reason behind the choice of a CNN to automate the process of image representation construction in our research. In this paper, we propose a new CNN-based method allowing to generate general image content representation. The image is described as a tuple of feature maps that describe the localization and intensity of selected visual features on the image plane. A feature map is a matrix that describes the intensity of a certain visual feature at every point of the image. This means that instead of global image features, such as _image is mostly blue_ or _there are many vertical lines_, we focus on local features, such as _there is a vertical segment centered at a point with given coordinates_. Due to the local nature of the selected features, the proposed method encodes each feature occurrence as a single matrix element.