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Recognizing cars is a highly difficult task due to the wide variety in the appearance of cars from the same car manufacturer. Therefore, the car logo is the most prominent indicator of the car manufacturer. The captured logo image suffers from several problems, such as a complex background, differences in size and shape, the appearance of noise, and lighting circumstances. To solve these problems, this paper presents an effective technique for extracting and recognizing a logo that identifies a car. Our proposed method includes four stages: First, we apply the kmedoids clustering method to extract the logo and remove the background and noise. Secondly, the logo image is converted to grayscale and also converted to a binary image using Otsu's method. Thirdly, the Daubechies wavelet with DCT transforms is applied to extract a feature vector for each image. Finally, the Canberra distance is used to match the tested image's feature vector to all feature vectors in the database. The test results indicate the highest CRR, accuracy, and precision at 99.37%, 99.39%, and 99.80%, respectively. This system is applicable to intelligent surveillance systems. [ABSTRACT FROM AUTHOR]

Brand logo recognition is a task focused on the identification and classification of logos, with various applications such as brand protection and market discovery. Since the number of brands is dynamic and evolves over time, we propose a two-step, zero-shot framework for this problem. In the first step, we train a Scaled-YOLOv4 single-stage universal logo detector to identify regions containing logo-like objects. Our results indicate that this detector achieves generalizability comparable to the two-stage Faster-RCNN model. In the second step, we employ an enhanced CLIP model for zero-shot classification of the identified regions. Our experiments demonstrate that the CLIP model outperforms state-of-the-art few-shot classifiers in terms of accuracy. Additionally, we adopt test-time augmentation to improve the model’s resistance against false positives. We also present a proof-of-concept for fine-tuning the CLIP model, which enhances its cosine similarity measures. Our proposed end-to-end solution is scalable in terms of the number of brands and requires only the brand names for detection. The logo detector achieves superior performance on the FlickrLogos-32 dataset without the need for additional fine-tuning.

Deep learning has gained high popularity in the field of image processing and computer vision applications due to its unique feature extraction property. For this characteristic, deep learning networks used to solve different issues in computer vision applications. In this paper the issue has been raised is classification of logo of formal directors in Iraqi government. The paper proposes a multi-layer convolutional neural network (CNN) to classify and recognize these official logos by train the CNN model on several logos. The experimental show the effectiveness of the proposed method to recognize the logo with high accuracy rate about 99.16%. The proposed multi-layers CNN model proves the effectiveness to classify different logos with various conditions. [ABSTRACT FROM AUTHOR]

Despite the continued growth of the Olympic Games (OG), the Youth Olympic Games (YOG) has received minimal attention from mainstream media since its introduction in 2010. The purpose of this study was to examine and compare event awareness and consumption intention for the 2012 Winter YOG to two international sport events occurring in the same year. A survey instrument was utilized to examine and compare event awareness, consumption intention, and logo identification for three international sport events within a millennial generation sample. The study showed significant differences in personal and public awareness between the three sport events, with personal (r = .313, p = .001) and public (r = .331, p = .001) awareness for the YOG demonstrating a positive correlation with consumption intention. This study is an important assessment of the YOG event awareness that can be utilized by the International Olympic Committee (IOC) to better understand and engage their participants and audience. Successful promotion of the YOG may require a transformation of the current marketing strategies that are utilized. The YOG has great opportunity for success in the global sport market to leave behind the status of the best kept secret in sport. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aiming at the problem of training sample sparse in Logo recognition task under the deep learning framework,a Logo data synthesis algorithm based on contexts was proposed.The algorithm comprehensively utilizes various types of context information to guide the synthesis of Logo images,such as the interior of Logo object,the neighborhood of Logo object,the link between Logo object and other objects and the scene where Logo object lives in.The experimental results on the FlickrLogos-32 dataset show that the proposed algorithm can improve the performance of the Logo identification algorithm (mAP increase by 8.5%) without relying on additional manual annotation,verifying the effectiveness of the synthesis algorithm.

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. [ABSTRACT FROM AUTHOR]

Pay TV has evolved from a walled garden, set-top box model to include online services. Although there are numerous operator and consumer benefits that result from this shift, it also opens up tremendous piracy threats that are a nightmare to control. The sheer volume of information being shared in a more open environment means that manpower alone is insufficient to process and detect threats effectively. As artificial intelligence (AI) technology develops in the media space, its application in security must focus on more than closing gaps and locking down assets. Security threats must be spotted and managed faster and more efficiently, before a security instance even occurs. In this paper, we explain how to leverage AI to fight piracy by using content monitoring solutions that search and identify pirated content on the internet. A t the core of this technology is an Al-powered computer vision system that identifies the original source of distributed content based on the visual information present in the image (e.g., broadcaster logo). We cover practical issues around building such a system, including its workflow, training, and performance. [ABSTRACT FROM AUTHOR]

In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried out on the FlickrLogos-32 database, and we evaluate the effect on recognition performance of synthetic versus real data augmentation, and image pre-processing. Moreover, we systematically investigate the benefits of different training choices such as class-balancing, sample-weighting and explicit modeling the background class (i.e. no-logo regions). Experimental results confirm the feasibility of the proposed method, that outperforms the methods in the state of the art. [ABSTRACT FROM AUTHOR]

The present research aims to better understand the impact of brand exposure and brand perception on logo recall and recognition. Building off of prior work that has examined surprising impairments in visual memory for the Apple logo (Blake, Nazarian and Castel, 2015), a comparative analysis is developed by comparing computer science and social science becoming specialists. The Apple logo is used as a case study due to its minimalism. The data reveal that only a small amount of the subjects can recall and recognize the Apple logo correctly. The recognition phase, in comparison with the recalling one, seems to be an easier task for the subjects. However, although the stylized features of the logo are often overlooked, each subject manages to recognize a large amount of details of the logo. Contrary to expectations, regardless of being a man or a woman, owning an Apple device, or developing a very positive emotional attachment to the brand are not significant variables that can determine a higher level of recall and recognition. Nevertheless, having a strong background in the technological domain can increase the probability of paying much attention to the details of a technical brand. [ABSTRACT FROM AUTHOR]

This paper presents a method for efficient and scalable logo recognition. Using generalized Hough transform to identify local features that are invariant across images, we can efficiently add spatial information into groups of local features and enhance the discriminative power of local feature. Our method is more flexible and efficient compared with state-of-the-art methods that merge features into groups. To fully exploit the information that different logo images provide, we employ a reference-based image representation scheme to represent training and testing images. Experiments on challenging datasets show that our method is efficient and scalable and achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Nowadays, information is provided through diverse network channels and, above all, its diffusion occurs in an always faster and pervasive manner. Social Media (SM) plays a crucial role in distributing, in an uncontrolled way, news, opinions, media contents and so on, and can basically contribute to spread information that sometimes are untrue and misleading. An integrated assessment of the trustworthiness of the information that is delivered is claimed from different sides: the Secure! project strictly fits in such a context. The project has been studying and developing a service oriented infrastructure which, by resorting at diverse technological tools based on image forensics, source reputation analysis, Twitter message trend analysis, web source retrieval and crawling, and so on, provides an integrated event assessment especially regarding crisis management. The aim of this paper is to present an interesting case-study which demonstrates the potentiality of the developed system to achieve a new integrated knowledge. [ABSTRACT FROM AUTHOR]

Recent years have shown us the quick development of social network. For companies, microblog platform is more and more important as one source to disseminate brand information and monitor their development. Compared with the frequently used text information existing in traditional media, microblog platform provides information about brands in more types such as images and other related information forms. According to the statistics, microblogs posted on social network contain more and more percentage of images. Hence how to recognize logos in images from social network is of high value. To address this problem, we propose a novel learning-based logo detection method with social network information assistance. A new dense histogram type feature is proposed to classify logo and non-logo image patches. To increase the detection precision, social network content is analyzed and employed to do filtering to reduce detection window candidates. Through the evaluation on large-scale data collected from Sina Weibo platform, the proposed method is demonstrated effective. [ABSTRACT FROM AUTHOR]

The paper presents a hybrid ensemble of diverse classifiers for logo and trademark symbols recognition. The proposed ensemble is composed of four types of different member classifiers. The first one compares color distribution of the logo patterns and is responsible for sifting out images of different color distribution. The second of the classifiers is based on the structural tensor recognition of local phase histograms. A proposed modification in this module consists of tensor computation in the space of the morphological scale-space. Thanks to this, more discriminative histograms describing global shapes are obtained. Next in the chain, is a novel member classifier that joins the Hausdorff distance with the correspondence measure of the log-polar patches computed around the corner points. This sparse classifier allows reliable comparison of even highly deformed patterns. The last member classifier relies on the statistical affine moment invariants which describe global shapes. However, a real advantage is obtained by joining the aforementioned base classifiers into a hybrid ensemble of classifiers, as proposed in this paper. Thanks to this a more accurate response and generalizing properties are obtained at reasonable computational requirements. Experimental results show good recognition accuracy even for the highly deformed logo patterns, as well as fair generalization properties which support human search and logo assessment tasks. [ABSTRACT FROM AUTHOR]

Finding logos in the real-world images is a challenging task due to their small size, simple shape, less texture and clutter background. In this paper, through visual logo analysis with different types of features, we propose a novel framework for finding visual logos in the real-world images. First, we exploit the contextual shape and patch information around feature points, merge them into a combined feature representation (point-context). Considering the characteristics of logos, this kind of fusion is an effective enhancement for the discriminability of single point features. Second, to eliminate the interference of the complex and noisy background, we transfer the logo recognition to a region-to-image search problem by segmenting real-world images into region trees. A weak geometric constraint based on regions is encoded into an inverted file structure to accelerate the search process. Third, we apply global features to refine initial results in the re-ranking stage. Finally, we combine each region score both in max-response and accumulate-response mode to obtain the final results. Performances of the proposed approach are evaluated on both our CASIA-LOGO dataset and the standard Flickr logos 27 dataset. Experiments and comparisons show that our approach is superior to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Logos are specially designed marks that identify goods, services, and organizations using distinguished characters, graphs, signals, and colors. Identifying logos can facilitate scene understanding, intelligent navigation, and object recognition. Although numerous logo recognition methods have been proposed for printed logos, a few methods have been specifically designed for logos in photos. Furthermore, most recognition methods use codebook-based approaches for the logos in photos. A codebook-based method is concerned with the generation of visual words for all the logo models. When new logos are added, the codebook reconstruction is required if effectiveness is a crucial factor. Moreover, logo detection in natural scenes is difficult because of perspective tilt and non-rigid deformation. Therefore, this study develops an extendable, but discriminating, model-based logo detection method. The proposed logo detection method is based on a support vector machine (SVM) using edge-based histograms of oriented gradient (HOGE) as features through multi-scale sliding window scanning. Thereafter, anti-distortion affine scale invariant feature transform (ASIFT) is used for logo verification with constraints on the ASIFT matching pairs and neighbors. The experimental results using the public Flickr-Logo database confirm that the proposed method has a higher retrieval and precision accuracy compared to existing model-based methods. [ABSTRACT FROM AUTHOR]