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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]