Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval

Using association rule mining to enrich semantic concepts for video retrieval

In order to achieve true content-based information retrieval on video we should analyse and index video with high-level semantic concepts in addition to using user-generated tags and structured metadata like title, date, etc. However the range of such high-level semantic concepts, detected either manually or automatically, usually limited compared to the richness of information content in video and the potential vocabulary of available concepts for indexing. Even though there is work to improve the performance of individual concept classifiers, we should strive to make the best use of whatever partial sets of semantic concept occurrences are available to us. We describe in this paper our method for using association rule mining to automatically enrich the representation of video content through a set of semantic concepts based on concept co-occurrence patterns. We describe our experiments on the TRECVid 2005 video corpus annotated with the 449 concepts of the LSCOM ontology. The evaluation of our results shows the usefulness of our approach

TAG ME: An Accurate Name Tagging System for Web Facial Images using Search-Based Face Annotation

Now a day the demand of social media is increases rapidly and most of the part of social media is made up of multimedia content cognate as images, audio, video. Hence for taking this as a motivation we have proffer a framework for Name tagging or labeling For Web Facial Images, which are easily obtainable on the internet. TAG ME system does that name tagging by utilizing search-based face annotation (SBFA). Here we are going to select an image from a database which are weakly labeled on the internet and the "TAG ME" assign a correct and accurate names or tags to that facial image, for doing this a few challenges have to be faced the One exigent difficulty for search-based face annotation strategy is how to effectually conduct annotation by utilizing the list of nearly all identical face images and its labels which is weak that are habitually rowdy and deficient. In TAGME we have resolve this problem by utilizing an effectual semi supervised label refinement (SSLR) method for purify the labels of web and nonweb facial images with the help of machine learning techniques. Secondly we used convex optimization techniques to resolve learning problem and used effectual optimization algorithms to resolve the learning task which is based on the large scale integration productively. For additionally quicken the given system, finally TAGME system proposed clustering-based approximation algorithm which boost the scalability considerably

Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos

This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part of the modern-day lives of millions of people. To achieve this goal, we apply state-of-the-art computational methods for (1) automatic emotion recognition from facial expressions, (2) extraction of modulations in the participants' speeches and (3) sentiment analysis from the closed caption associated to the videos of interest. More specifically, we compute features, such as, visual intensities of recognized emotions, field sizes of participants, voicing probability, sound loudness, speech fundamental frequencies and the sentiment scores (polarities) from text sentences in the closed caption. Experimental results with a dataset containing 520 annotated news videos from three Brazilian and one American popular TV newscasts show that our approach achieves an accuracy of up to 84% in the sentiments (tension levels) classification task, thus demonstrating its high potential to be used by media analysts in several applications, especially, in the journalistic domain.Comment: 5 pages, 1 figure, International AAAI Conference on Web and Social Medi

Labeling Faces Victimization Bunch Primarily Based Internet Pictures Annotation to Produce Authentication in Security

Auto face annotation is important in abounding absolute apple advice administration systems. Face tagging in images and videos enjoys abounding abeyant applications in multimedia advice retrieval. Face comment is a meadow of face apprehension and recognition. Mining abominably labeled facial images on the internet shows abeyant classic appear auto face annotation. This blazon of classic motivates the new assay botheration of defended authentication. The ambition of the arrangement is to comment disregarded faces in images and videos with the words that best alarm the image. A framework called seek based face comment (SBFA) provides the way to abundance abominably labeled facial images. Facial images that are accessible on Apple Wide Web (WWW) or the angel database created by the aegis administration can be annotated. A one arduous botheration with the seek based face comment arrangement is how finer accomplish comment by advertisement agnate facial images and their anemic labels which are blatant and incomplete. To affected this botheration proposed admission uses unsupervised characterization clarification (ULR) to clarify the labels of web facial images. To acceleration up the proposed arrangement a absorption based approximation algorithm is used. Uses of comment will advice for user to seek admiration angel and video. As well if arrangement gets implemented in amusing arrangement again it will affected the check of accepted absolute arrangement which tags manually

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Taking the bite out of automated naming of characters in TV video

We investigate the problem of automatically labelling appearances of characters in TV or film material with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”