Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images sent from mobile devices. In this paper, we propose a novel hashing scheme, named as canonical view based discrete multi-modal hashing (CV-DMH), to handle these problems via a novel three-stage learning procedure. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multi-modal sparse coding is applied to transform visual features from multiple modalities into an intermediate representation. It can robustly and adaptively characterize visual contents of varied landmark images with certain canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises. In this part, we develop a new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes. We can not only explicitly deal with the discrete constraint, but also consider the bit-uncorrelated constraint and balance constraint together. Experiments on real world landmark datasets demonstrate the superior performance of CV-DMH over several state-of-the-art methods

Object Tracking in Vary Lighting Conditions for Fog based Intelligent Surveillance of Public Spaces

With rapid development of computer vision and artificial intelligence, cities are becoming more and more intelligent. Recently, since intelligent surveillance was applied in all kind of smart city services, object tracking attracted more attention. However, two serious problems blocked development of visual tracking in real applications. The first problem is its lower performance under intense illumination variation while the second issue is its slow speed. This paper addressed these two problems by proposing a correlation filter based tracker. Fog computing platform was deployed to accelerate the proposed tracking approach. The tracker was constructed by multiple positions' detections and alternate templates (MPAT). The detection position was repositioned according to the estimated speed of target by optical flow method, and the alternate template was stored with a template update mechanism, which were all computed at the edge. Experimental results on large-scale public benchmark datasets showed the effectiveness of the proposed method in comparison with state-of-the-art methods

CYBERBULLING I CYBER-MOBBING: PRAVNA PITANJA U PRAKSI ZEMALJA U RAZVOJU

The article subject is cyberbullying and cybermobbing. The emphasis is placed on the legal practice of combating cyberbullying and cybermobbing issues in developing countries, since these phenomena are still insufficiently studied. The developing countries legislation is compared with doctrinal and practical developments in the fight against the studied problem in developed countries of the West and former USSR. Experiment was conducted to determine the methods effectiveness to combat cyberbullying using the social networks built-in extensions. 40 random accounts were taken in equal parts related to "male" and "female" representatives, from 18 to 30 years old. The article indicates cyber-mobbing and cyberbullying concepts and their varieties, existing in modern world. The study examines statistical data, programs and measures of different states in fight against cyberbullying and cyber-mobbing. Experiments results showed that Instagram users are aware of the built-in extensions availability of the social network to protect against cyberbullying and use them relatively frequently. With that, female segment of Instagram users is more concerned about comments content under their photos than the male one. Measures have been developed to prevent and counteract cyberbullying and cybermobbing, introduction of which into the states policies might help in the fight against these social phenomena.Tema članka je cyber maltretiranje i cyber mobing. Naglasak je stavljen na pravnu praksu borbe protiv cyber bullyinga i cyber mobinga u zemljama u razvoju, budući da su ti fenomeni još uvijek nedovoljno proučeni. Zakonodavstvo zemalja u razvoju uspoređuje se s doktrinarnim i praktičnim dostignućima u borbi protiv proučavanog problema u razvijenim zemljama Zapada i bivšeg SSSR-a. Eksperiment je proveden kako bi se utvrdila učinkovitost metoda za borbu protiv internetskog nasilja pomoću ugrađenih proširenja društvenih mreža. Uzeto je 40 slučajnih računa u jednakim dijelovima koji se odnose na "muške" i "ženske" predstavnike, stare od 18 do 30 godina. Članak ukazuje na koncepte cyber-mobinga i cyber bullyinga i njihove sorte koji postoje u modernom svijetu. Studija istražuje statističke podatke, programe i mjere različitih država u borbi protiv cyber bullyinga i cyber mobinga. Rezultati eksperimenata pokazali su da su korisnici Instagrama svjesni ugrađenih proširenja dostupnih na društvenoj mreži kako bi se zaštitili od internetskog zlostavljanja i koriste ih relativno često. Uz to, ženski segment korisnika Instagrama više brine sadržaj komentara ispod njihovih fotografija nego muški. Razvijene su mjere za sprečavanje i suzbijanje cyber maltretiranja i cyber mobinga, čije uvođenje u politike država može pomoći u borbi protiv ovih društvenih pojava

Bi-level semantic representation analysis for multimedia event detection

© 2013 IEEE. Multimedia event detection has been one of the major endeavors in video event analysis. A variety of approaches have been proposed recently to tackle this problem. Among others, using semantic representation has been accredited for its promising performance and desirable ability for human-understandable reasoning. To generate semantic representation, we usually utilize several external image/video archives and apply the concept detectors trained on them to the event videos. Due to the intrinsic difference of these archives, the resulted representation is presumable to have different predicting capabilities for a certain event. Notwithstanding, not much work is available for assessing the efficacy of semantic representation from the source-level. On the other hand, it is plausible to perceive that some concepts are noisy for detecting a specific event. Motivated by these two shortcomings, we propose a bi-level semantic representation analyzing method. Regarding source-level, our method learns weights of semantic representation attained from different multimedia archives. Meanwhile, it restrains the negative influence of noisy or irrelevant concepts in the overall concept-level. In addition, we particularly focus on efficient multimedia event detection with few positive examples, which is highly appreciated in the real-world scenario. We perform extensive experiments on the challenging TRECVID MED 2013 and 2014 datasets with encouraging results that validate the efficacy of our proposed approach