Comparison of Semantic Similarity Measures for                                    NDVC Detection Using Semantic Features  ...
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Comparison of Semantic Similarity Measures for NDVC Detection Using Semantic Features

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Comparison of Semantic Similarity Measures for NDVC Detection Using Semantic Features

  1. 1. Comparison of Semantic Similarity Measures for NDVC Detection Using Semantic Features Hyun-seok Min, Jae Young Choi, Wesley De Neve, and Yong Man Ro Image and Video Systems Lab Korea Advanced Institute of Science and Technology (KAIST) Daejeon, South Korea e-mail: ymro@ee.kaist.ac.kr website: http://ivylab.kaist.ac.krI. INTRODUCTION 1.3. Jiang–Conrath : based on the conditional probability of encountering- Observations an instance of a child concept in a certain corpus - an increasing number of near-duplicate video clips (NDVCs) can be found on websites for video sharing 1 simJC (ti , t j ) = . - content transformations tend to preserve semantic information log( p(ti )) + log( p(t j )) - log(p(lso(ti , t j )))- Novel idea - NDVC detection by means of semantic features and adaptive 1.4. Lin : follows from his theory of similarity between arbitrary objects semantic distance measurement- Objective 2 × log p(lso(ti , t j )) - to answer the question: ‘which semantic similarity measure is most simL (ti , t j ) = . effective in the context of NDVC detection using semantic features?’ log p(ti ) + log p(t j )II. SEMANTIC NDVC DETECTION 2. Similarity measurement using Flickr tag occurrence and co-occurrence Input: query video clip statistics Video shot segmentation Image folksonomy I ti ∩ j I ti ∩ j : the set of images annotated with both t t ti and tj simTC (ti , t j ) = , ... ... I ti I ti : the set of images annotated with tag ti Tag relevance learning Shot 1 ... Shot i ... Shot N using neighbor voting IV. EXPERIMENTS Semantic concept detection 1. Experimental setup ... ... - Use of TRECVID 2009 for creating NDVCs and reference video clips Creation of a semantic video signature - Use of MIRFLICKR-25000 as a source of collective knowledge - Use of Toolbox and the Natural Language Toolkit (NLTK) for WordNet- Matching of semantic video signatures based semantic similarity measurement Reference video 2. Experimental results Output: NDVC identification database - Semantic NDVC detection is, in general, most effective when similarity measurement makes use of tag statistics derived from Flickr Fig. 1. NDVC detection by means of semantic video signatures. - similarity measurement using Flickr-based tag statistics is able to   exploit an unrestricted concept vocabulary, whereas the WordNet- Ai  ti , j , wi , j , j  1,..., Ai , wi , j is a weight value for tag ti,j based similarity measures are only able to make use of semantic concepts that are part of the English-language version of WordNet 0.8 q r q r q r q r T Tag statistics Leacock–ChodorowDshot (S , S ) = SQFD( A , A ) = w | -w G w | -w , 0.7 Jiang–Conrath Lin 0.6 Resnik SQFD: Signature Quadratic Form Distance 0.5 NDCR W: vector of weight values for the tags t under consideration 0.4 G: matrix of ground distances (computed using tag statistics) 0.3 III. SEMANTIC SIMILARITY MEASURES 0.2 0.1 1. Similarity measurement using the WordNet knowledge base 0 blur crop pattern change in mirroring resize shift average 1.1. Leacock–Chodorow : relies on the length of the shortest path insertion brightness between two concepts Transformations len(ti , t j ) simLC (ti , t j ) = log , Fig. 2. Influence of semantic similarity measurement on the effectiveness of semantic 2E NDVC detection. The lower the NDCR, the more effective NDVC detection. len(ti , t j ) : the shortest path between two concepts (ti, tj) V. CONCLUSIONS E : the overall depth of the taxonomy used - We presented a novel technique for NDVC detection 1.2. Resnik : measures the information content of the most specific - takes advantage of the collective knowledge in an image folksonomy, common ancestor of two concepts thus allowing for the use of an unrestricted concept vocabulary - We quantified the influence of several semantic similarity measures on simR (ti , t j ) = log p(lso(ti , t j )), the effectiveness of NDVC detection using semantic features - semantic NDVC detection is most effective when semantic similarity lso(ti , t j ) : the lowest super-ordinate of ti and tj measurement takes advantage of tag occurrence and co-occurrence statistics derived from Flickr (an unstructured source of knowledge), p(t ) : the probability of encountering an instance of a concept t outperforming semantic similarity measurement that takes advantage in a certain corpus of WordNet (a knowledge base with a hierarchical structure) The International Conference on Multimedia Information Technology and Applications (MITA), July 2012, Beijing (China)

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