Artificial intelligence technologies, Image and vision computing, Digital signal processing

自编码器是在机器学习的许多领域中流行的神经网络。它们由两个主要部分组成-编码器和解码器。前者将输入数据转换为矢量表示，后者从中恢复数据。自动编码器的主要目标是最小化输入和恢复数据之间的差异。同样重要的是潜在表示，它是编码器输出的向量。该向量包含重建数据所需的基本低维信息，因此自动编码器通常用于数据压缩。然而，在许多应用中，恢复原始数据是不必要的，例如某些计算机视觉任务，包括图像分类或检索，其中，仅需要图像表示就可以从图像中进行准确的预测。所提出的研究重点是在应用场景所施加的极端约束下的图像检索，例如，具有噪声的通信信道，其中只有少量的数据可以在有限的时间内发送。将研究深度学习（基于深度神经网络）方法来执行这些任务，并将约束和噪声模型纳入系统中。将考虑两个具体的应用场景：人员重新识别和人脸识别闭路电视在监控任务中，大量的数据需要有效地访问，因此内存和噪音的限制。人脸识别是一个类似的任务，以人的重新识别，但需要编码的成功识别的细节类型不同。各种方法将被测试来执行这些任务。作为一个基线，传统的图像压缩方法将被用来满足信道带宽的限制和检索任务将恢复（解压缩）的图像上执行。随后，压缩算法将被自动编码器所取代，在自动编码器中，将模拟潜在表示的传输，并将解码器重建的图像输入检索网络。替代方法将包括将表示编码器应用于原始图像并模拟所得到的特征向量的传输。编码方法的有效性将通过识别方法的性能和编码表示的紧凑性来评估，同时受到各种噪声的影响。本研究的主要目标是研究深度学习自动编码器模型的噪声、检索性能和表示压缩之间的关系，并将其与仅基于发送特征向量的替代方法进行比较。编码特定的和面向任务的图像表示在嘈杂的通信信道下的问题还没有被研究。将噪声通信信道模型结合到其设计中的自动编码器相对较新，并且通常使用标准度量（例如MSE和PSNR）进行评估（脉冲信噪比），而这项研究将考虑面向任务的，因此更可靠的指标，即检索性能。这项研究是相关的许多监测方案，其中需要在有限时间内经由无线通信信道发送大量数据。这种应用的一个例子是使用配备有摄像机的无人机在一大群人中搜索嫌疑人。由该应用引入的约束（诸如使用低功率发射器使得所发射的信号易受噪声影响）以及所发射的信息的高压缩水平对通常为人类识别任务提出的方法提出了新的挑战。他们需要新的方法来编码图像表示，是强大的噪声和面向任务的，只保留任务相关的信息。

Autoencoders are neural networks popular in many areas of Machine Learning. They are built of two main parts - encoder and decoder. The former transforms input data into a vector representation and the latter recovers the data from it. The main objective of the autoencoders is to minimize difference between the input and the recovered data. Equally important is the latent representation, which is a vector output by the encoder. This vector contains essential, low-dimensional information needed to reconstruct the data, therefore autoencoders are often used for data compression. However, there are many applications, where recovering the original data is not necessary such as certain Computer Vision tasks, including image classification or retrieval, where only the image representation is needed to enable accurate prediction from the image.The focus of the proposed research is on image retrieval under extreme constraints imposed by the application scenario e.g. communication channel with noise where only a small amount of data can be transmitted in a limited time. Deep learning (based on deep neural networks) methods will be investigated to perform these tasks, and the constraints as well as a noise model will be incorporated into the system. Two specific application scenarios will be considered: person re-identification and face recognition CCTV in surveillance tasks where large volumes of data need to be accessed efficiently, hence the memory and noise constraints. Face recognition is a similar task to person re-identification, but the type of details needed to be encoded for successful identification differ.Various approaches will be tested to perform these tasks. As a baseline, conventional image compression methods will be used to meet channel bandwidth limitations and retrieval tasks will be performed on the recovered (decompressed) images. Subsequently, compression algorithms will be replaced by autoencoder, where latent representation's transmission will be simulated, and images reconstructed by the decoder will be fed into the retrieval network. Alternative approach will consist of applying the representation encoders to the original images and simulating transmission of the resulting feature vector. The effectiveness of the encoding approach will be assessed by the performance of the identification methods and the compactness of the encoded representation while subjected to various amounts of noise.The main objective of this research will be to study the relation between noise, retrieval performance, and representation compression for deep learning autoencoder models and compare it to the alternative approach based on sending feature vectors only. The problem of encoding specific and task-oriented image representation under noisy communication channel has not been investigated yet. Autoencoders that incorporate noisy communication channel models into their designs are relatively new and typically evaluated with standard metrics, such as MSE and PSNR (Pulse Signal to Noise Ratio), whereas this research will consider task-oriented thus more reliable metrics, namely retrieval performances.This research is relevant to numerous surveillance scenarios, where it is necessary to transmit large amounts of data via wireless communication channel within a limited time. An example of such application is using a drone equipped with a video camera and searching for a suspect in a large crowd. The constraints introduced by this application such as the use of low power transmitters making the transmitted signal vulnerable to noise, and high compression level of the transmitted information pose new challenges on the methods typically proposed for the human identification tasks. They require new methods of encoding image representations that are robust to noise and task-oriented for retaining only the task relevant information.