This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-k image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we proposed a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the state-of-the-art techniques with similar accuracy.
Massive amount of multimedia data that contain times-tamps and geographical information are being generated at an unprecedented scale in many emerging applications such as photo sharing web site and social networks applications. Due to their importance, a large body of work has focused on efficiently computing various spatial image queries. In this paper,we study the spatial temporal image query which considers three important constraints during the search including time recency, spatial proximity and visual relevance. A novel index structure, namely Hierarchical Information Quadtree(HI-Quadtree), to efficiently insert/delete spatial temporal images with high arrive rates. Base on HI-Quadtree an efficient algorithm is developed to support spatial temporal image query. We show via extensive experimentation with real spatial databases clearly demonstrate the efficiency of our methods.
With advances in multimedia technologies and the proliferation of smart phone, digital cameras, storage devices, there are a rapidly growing massive amount of multimedia data collected in many applications such as multimedia retrieval and management system, in which the data element is composed of text, image, video and audio. Consequently, the study of multimedia near duplicate detection has attracted significant concern from research organizations and commercial communities. Traditional solution minwish hashing (MinWise) faces two challenges: expensive preprocessing time and lower comparison speed. Thus, this work first introduce a hashing method called one permutation hashing (OPH) to shun the costly preprocessing time. Based on OPH, a more efficient strategy group based one permutation hashing (GOPH) is developed to deal with the high comparison time. Based on the fact that the similarity of most multimedia data is not very high, this work design an new hashing method namely hierarchical one permutation hashing (HOPH) to further improve the performance. Comprehensive experiments on real multimedia datasets clearly show that with similar accuracy HOPH is five to seven times faster than MinWise.
Due to the advances in mobile computing and multimedia techniques, there are vast amount of multimedia data with geographical information collected in multifarious applications. In this paper, we propose a novel type of image search named interactive geo-tagged image search which aims to find out a set of images based on geographical proximity and similarity of visual content, as well as the preference of users. Existing approaches for spatial keyword query and geo-image query cannot address this problem effectively since they do not consider these three type of information together for query. In order to solve this challenge efficiently, we propose the definition of interactive top-k geo-tagged image query and then present a framework including candidate search stage , interaction stage and termination stage. To enhance the searching efficiency in a largescale database, we propose the candidate search algorithm named GI-SUPER Search based on a new notion called superior relationship and GIR-Tree, a novel index structure. Furthermore, two candidate selection methods are proposed for learning the preferences of the user during the interaction. At last, the termination procedure and estimation procedure are introduced in brief. Experimental evaluation on real multimedia dataset demonstrates that our solution has a really high performance.2 Jun Long et al.Keywords Geo-tagged multimedia data · Interactive query · Top-k spatial search IntroductionWith the rapid development of mobile Internet and social multimedia applications, huge amount of multimedia data [42] such as text, image, audio and video have been generated and shared everyday. For instance, Facebook 1 reports 350 million photos uploaded daily as of November 2013. More than 400 million daily tweets containing texts and images have been generated by 140 million Twitter 2 active users. Flickr 3 had a total of 87 million registered members and more than 3.5 million new images uploaded daily in March 2013. Large-scale of multimedia data stored in massive databases lead to multifarious innovative Internet services.Just as an English idiom said, a picture is worth a thousand words, that means a image contains much more information than a sentence or a few words. Keyword-based image retrieval methods have the limitation that they have to depend on manual annotation. Obviously, it is impractical that annotating all image data in a large-scale database manually. Besides, we cannot use a few of keywords to describe most of photos comprehensively. Unlike this traditional technique, content-based image retrieval (CBIR for short) has extensive applications, which use inherent visual content of images for searching and query. Over the last decade, lots of researchers have been attracted by CBIR techniques in the area of multimedia information retrieval [45,51] and many CBIR systems like K-DIME [4],IRMFRCAMF [26], gMRBIR [7] have been developed for constructing multimedia intelligent systems.Modern mobile computing devices like smartphones and tablets are equipped with hi...
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