Automatic Logo Research Articles

A Multi-Stage Method for Logo Detection in Scanned Official Documents Based on Image Processing

A logotype is a rectangular region defined by a set of characteristics, which come from the pixel information and region shape, that differ from those of the text. In this paper, a new method for automatic logo detection is proposed and tested using the public Tobacco800 database. Our method outputs a set of regions from an official document with a high probability to contain a logo using a new approach based on the variation of the feature rectangles method available in the literature. Candidate regions were computed using the longest increasing run algorithm over the document blank lines’ indices. Those regions were further refined by using a feature-rectangle-expansion method with forward checking, where the rectangle expansion can occur in parallel in each region. Finally, a C4.5 decision tree was trained and tested against a set of 1291 official documents to evaluate its performance. The strategic combination of the three previous steps offers a precision and recall for logo detention of 98.9% and 89.9%, respectively, being also resistant to noise and low-quality documents. The method is also able to reduce the processing area of the document while maintaining a low percentage of false negatives.

Long-Range Dependence Involutional Network for Logo Detection

Logo detection is one of the crucial branches in computer vision due to various real-world applications, such as automatic logo detection and recognition, intelligent transportation, and trademark infringement detection. Compared with traditional handcrafted-feature-based methods, deep learning-based convolutional neural networks (CNNs) can learn both low-level and high-level image features. Recent decades have witnessed the great feature representation capabilities of deep CNNs and their variants, which have been very good at discovering intricate structures in high-dimensional data and are thereby applicable to many domains including logo detection. However, logo detection remains challenging, as existing detection methods cannot solve well the problems of a multiscale and large aspect ratios. In this paper, we tackle these challenges by developing a novel long-range dependence involutional network (LDI-Net). Specifically, we designed a strategy that combines a new operator and a self-attention mechanism via rethinking the intrinsic principle of convolution called long-range dependence involution (LD involution) to alleviate the detection difficulties caused by large aspect ratios. We also introduce a multilevel representation neural architecture search (MRNAS) to detect multiscale logo objects by constructing a novel multipath topology. In addition, we implemented an adaptive RoI pooling module (ARM) to improve detection efficiency by addressing the problem of logo deformation. Comprehensive experiments on four benchmark logo datasets demonstrate the effectiveness and efficiency of the proposed approach.

Automatic Logo Detection with Deep Region-based Convolutional Networks

Abstract: Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data. It makes this process faster and easier. We present a deep learning system for automatic logo detection in real world images. We base our detector on the popular framework of Faster R-CNNand compare its performance to other models such as Mask R-CNN or RetinaNet. We perform a detailed empirical analysis of various design and architecture choices and show how these can have much higher influence than algorithmic tweaks or popular techniques such as data augmentation. We also provide a systematic detection performance comparison of various models on multiple popular datasets including FlickrLogos-32, TopLogo-10 and recently introduced QMUL-OpenLogo benchmark, which allows for a direct comparison between recently proposed extensions. By careful optimization of the training procedure we were able to achieve significant improvements of the state of the art on all mentioned datasets. We apply our observations to build a detector to detect logos of the Red Bull brand in online media and images. Automatic logo detection and recognition is a challenging computer vision task with many applications. One of the typical purposes of such systems is measurement of brand visibility and prominence in online media or television broadcasts.

Automatic logo detection from document image using HOG features

Automatic logo detection from document image using HOG features

Domain Adaptation Deep Attention Network for Automatic Logo Detection and Recognition in Google Street View

Signboards are important location landmarks that provide services to a local community. Non-disabled people can easily understand the meaning of a signboard based on its special shape; however, visually impaired people who need an assistive system to guide them to destinations or to help them understand their surroundings cannot. Currently, designing accurate assistive systems remain a challenge. Computer vision struggles to recognize signboards due to the diverse designs that combine text and images. Moreover, there is a lack of datasets to train the best model and reach good results. In this paper, we propose a novel framework that can automatically detect and recognize signboard logos. In addition, we utilize Google Street View to collect signboard images from Taiwan's streets. The proposed framework consists of a domain adaptation that not only reduces the loss function between source-target datasets, but also represents important source features adopted by the target dataset. In our model, we add nonlocal blocks and attention mechanisms called deep attention networks to achieve the best final result. We perform extensive experiments on both our dataset and public datasets to demonstrate the superior performance and effectiveness of our proposed method. The experimental results show that our proposed method outperforms state-of-the-art methods across all evaluation metrics.

Patch-CNN: Deep learning for logo detection and brand recognition

Automatic logo detection and recognition is significantly growing due to the increasing requirements of intelligent documents analysis and retrieval. The main problem to logo detection is intra-class variation, which is generated by the variation in image quality and degradation. The problem of misclassification also occurs while having tiny logo in large image with other objects. To address this problem, Patch-CNN is proposed for logo recognition which uses small patches of logos for training to solve the problem of misclassification. The classification is accomplished by dividing the logo images into small patches and threshold is applied to drop no logo area according to ground truth. The architectures of AlexNet and ResNet are also used for logo detection. We propose a segmentation free architecture for the logo detection and recognition. In literature, the concept of region proposal generation is used to solve logo detection, but these techniques suffer in case of tiny logos. Proposed CNN is especially designed for extracting the detailed features from logo patches. So far, the technique has attained accuracy equals to 0.9901 with acceptable training and testing loss on the dataset used in this work.

Logo Recognition with the Use of Deep Convolutional Neural Networks

Automatic logo recognition is gaining importance due to the increasing number of its applications. Unlike other object recognition tasks, logo recognition is more challenging because of the limited amount of the available original data. In this paper, the transfer leaning technique was applied to a Deep Convolutional Neural Network model to guarantee logo recognition using a small computational overhead. The proposed method was based on the Densely Connected Convolutional Networks (DenseNet). The experimental results show that for the FlickrLogos-32 logo recognition dataset, our proposed method performs comparably with state-of-the-art methods while using fewer parameters.

Implicit and explicit identification of counterfeit brand logos based on logotype transposition

PurposeWith trade amounting to more than US$400bn, counterfeiting is already affecting many successful brands. Often, consumers are deceived into buying fake products due to the visual similarity between fake and original brand logos. This paper aims to explore the varying forms of fraudulent imitation of original brand logotypes (operationalized at the level of logotype transposition), which can aid in the detection of a counterfeit brand.Design/methodology/approachAcross two studies, this research tested how well consumers can differentiate counterfeit from original logos of well-known brands both explicitly and implicitly. Seven popular brand logos were altered to create different levels of visual dissimilarity and participants were required to discriminate the logos as fake or genuine.FindingsResults demonstrate that although consumers can explicitly discriminate fake logos with a high degree of accuracy, the same is not true under conditions in which logos are presented very briefly (tapping participants’ implicit or automatic logo recognition capabilities), except when the first and last letters of the logotype are substituted.Originality/valueA large body of research on counterfeit trade focuses on the individual or cross-cultural differences behind the prevalence of counterfeit trade. There is limited research exploring the ability of a consumer to correctly identify a fake logo, based on its varying similarity with the original logotype; this paper addresses this gap. Given that many of the purchase decisions are often made automatically, identifying key implicit differentiators that can help a consumer recognize a fake logo should be informative to both practitioners and academics.

The Role and Utilization of CNN in Automatic Logo Based Document Image Retrieval Methods

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.

A Review on Signature and Logo Identification and Extraction using Automatic Logo Based Document Image Retrieval Methods

A Review on Signature and Logo Identification and Extraction using Automatic Logo Based Document Image Retrieval Methods

A Review on Classification and Comparison of Automatic Logo Based Document Image Retrieval Methods and other Applications

A Review on Classification and Comparison of Automatic Logo Based Document Image Retrieval Methods and other Applications

Smart learning of logo detection for mobile phone applications

With the advance of mobile phone cameras and broadband networks, gaining access to digital information and services via logo recognition has become of high industrial interest. The fundamental subsystem for logo recognition must be a logo database, whose images link real-world information to specific corporate entities. However, few attempts have been made to create and update such a logo database, i.e., how to automatically collect the latest logos. Moreover, the few existing methods are limited in their application and unattractive in terms of logo detection accuracy and performance overhead. In this article, we describe a practical system for automatic logo extraction. Websites are an optimal source of a huge number of up-to-date logos, and experts can easily find logos from webpages without rendering. For instance, an expert can locate elements with the term "logo" using the websites' entity names as attribute values, and then download images connected to them. Our system mimics this human behavior to automate logo extraction. Given a website, it learns its entity name and uses that name to locate elements that lead to the logo. Evaluation tests showed that this contextual reasoning significantly contributes to the performance of the system, which achieved high precision with negligible overhead.

Exemplar-based logo and trademark recognition

Automatic logo recognition facilitates the process of searching and analyzing digital image-based resources. This is due to the simplicity and informativity of logos which can be used as a visual or semantic clue for applications such as content-based image retrieval. For automatic shape recognition, model-based approaches have gained a lot of attention due to their theoretical background and promising results. However, one of the major challenges of these approaches for logo recognition is collecting samples of logos with possible perspective tilts and non-rigid deformations that most likely happen to natural images. Therefore, a robust model cannot be trained due to the lack of sufficient samples. In this paper, we aim to overcome, or at least alleviate, this challenge by introducing an exemplar-based approach. The proposed method synthesizes new images of each logo of interest, so-called exemplars, by simulating two camera axes. This is to produce almost all possible views of a logo that may appear in a given image and guarantee that most of the distortions in the shape of a logo caused by a variation of the camera optical axis direction are covered. Subsequently, an exemplar-based model is derived based on all the exemplars using linear SVMs. Finally, for the detection and recognition, we associate observed logos with the exemplars using our exemplar-based model. Experiments are carried out on two publicly available logo datasets: FlickrLogos-27 and FlickrLogos-32. The results show the superior detection and recognition capabilities of the proposed method over the competitors.

Automatic logo based document image retrieval

Recently, there is need for the automatic logo detection and document image retrieval because of increasing requirements of intelligent document images. In this work, we have developed an automatic logo based document image retrieval system in which we first proposed an automatic logo detection and extraction from the document images using discrete wavelet transform (DWT). Then, we have proposed an approach to retrieve document images based on the logo using combined rotated complex wavelet filters (RCWF) and dual tree complex wavelet transform (DT-CWT). Since these combined features give information in twelve different directions. Our approach is segmentation free. Finally, we evaluate the effectiveness of our approach using large database collections of real-world complex documents.

Automatic logo transition detection in digital video contents

The amount of user created contents has been increasing rapidly and is associated with a serious copyright problem. Automatic logo detection and recognition in videos is a natural and efficient way of overcoming the copyright problem. However, logos have varying characteristics, which make logo detection and recognition very difficult. Moreover, logo transitions between two different logos exist in one video comprising several video contents. This disrupts the automatic logo detection and recognition. Therefore, in order to improve logo detection, it is necessary to take into account the logo transitions explicitly. This paper proposes an accurate logo transition detection method for recognizing logos in digital video contents. The proposed method accurately segments a video according to logo and efficiently recognizes various types of logos. The experimental results demonstrate the effectiveness of the proposed method for logo detection and video segmentation according to logo.