Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab's mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.
Neural networks have played a part in logo detection as well. @cite_4 incorporated neural networks by applying a probabilistic neural network on top of SIFT for vehicle model recognition. Francesconi @cite_13 used recursive neural networks to classify black-and-white logos. Duffner & Garcia @cite_9 fed pixel values directly into a convolutional neural network with two convolution layers to detect watermarks on television.
@cite_13 @cite_9 @cite_4
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In this paper we propose recognizing logo images by using an adaptive model referred to as recursive artificial neural network. At first, logo images are converted into a structured representation based on contour trees. Recursive neural networks are then learnt using the contourtrees as inputs to the neural nets. On the other hand, the contour-tree is constructed by associating a node with each exterior or interior contour extracted from the logo instance. Nodes in the tree are labeled by a feature vector, which describes the contour by means of its perimeter, surrounded area, and a synthetic representation of its curvature plot. The contour-tree representation contains the topological structured information of logo and continuous values pertaining to each contour node. Hence symbolic and sub-symbolic information coexist in the contour-tree representation of logo image. Experimental results are reported on 40 real logos distorted with artificial noise and performance of recursive neural network is compared with another two types of neural approaches. In this paper, we present a connectionist approach for detecting and precisely localizing transparent logos in TV programs. Our system automatically synthesizes simple problem-specific feature extractors from a training set of logo images, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the logo pattern to analyze. We present in detail the design of our architecture, our learning strategy and the resulting process of logo detection. We also provide experimental results to illustrate the robustness of our approach, that does not require any local preprocessing and leads to a straightforward real time implementation. This paper deals with a novel vehicle manufacturer and model recognition scheme, which is enhanced by color recognition for more robust results. A probabilistic neural network is assessed as a classifier and it is demonstrated that relatively simple image processing measurements can be used to obtain high performance vehicle authentication. The proposed system is assisted by a previously developed license plate recognition, a symmetry axis detector and an image phase congruency calculation modules. The reported results indicate a high recognition rate and a fast processing time, making the system suitable for real-time applications.