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A multiple relevance feedback strategy with positive and negative models
- PloS ONE
"... A commonly used strategy to improve search accuracy is through feedback techniques. Most existing work on feedback relies on positive information, and has been extensively studied in information retrieval. However, when a query topic is difficult and the results from the first-pass retrieval are ver ..."
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A commonly used strategy to improve search accuracy is through feedback techniques. Most existing work on feedback relies on positive information, and has been extensively studied in information retrieval. However, when a query topic is difficult and the results from the first-pass retrieval are very poor, it is impossible to extract enough useful terms from a few positive documents. Therefore, the positive feedback strategy is incapable to improve retrieval in this situation. Contrarily, there is a relatively large number of negative documents in the top of the result list, and it has been confirmed that negative feedback strategy is an important and useful way for adapting this scenario by several recent studies. In this paper, we consider a scenario when the search results are so poor that there are at most three relevant documents in the top twenty documents. Then, we conduct a novel study of multiple strategies for relevance feedback using both positive and negative examples from the first-pass retrieval to improve retrieval accuracy for such difficult queries. Experimental results on these TREC collections show that the proposed language model based multiple model feedback method which is generally more effective than both the baseline method and the methods using only positive or negative model.
Realistic Real-Time Outdoor Rendering in Augmented Reality
"... Realistic rendering techniques of outdoor Augmented Reality (AR) has been an attractive topic since the last two decades considering the sizeable amount of publications in computer graphics. Realistic virtual objects in outdoor rendering AR systems require sophisticated effects such as: shadows, day ..."
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Realistic rendering techniques of outdoor Augmented Reality (AR) has been an attractive topic since the last two decades considering the sizeable amount of publications in computer graphics. Realistic virtual objects in outdoor rendering AR systems require sophisticated effects such as: shadows, daylight and interactions between sky colours and virtual as well as real objects. A few realistic rendering techniques have been designed to overcome this obstacle, most of which are related to non real-time rendering. However, the problem still remains, especially in outdoor rendering. This paper proposed a much newer, unique technique to achieve realistic real-time outdoor rendering, while taking into account the interaction between sky colours and objects in AR systems with respect to shadows in any specific location, date and time. This approach involves three main phases, which cover different outdoor AR rendering requirements. Firstly, sky colour was generated with respect to the position of the sun. Second step involves the shadow generation algorithm, Z-Partitioning: Gaussian and Fog Shadow Maps (Z-GaF Shadow Maps). Lastly, a technique to integrate sky colours and shadows through its effects on virtual objects in the AR system, is introduced. The experimental results reveal that the proposed technique has
Signal Processing] (]]]])]]]–]]] Contents lists available at SciVerse ScienceDirect Signal Processing
"... journal homepage: www.elsevier.com/locate/sigpro Residual enhanced visual vector as a compact signature for mobile ..."
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journal homepage: www.elsevier.com/locate/sigpro Residual enhanced visual vector as a compact signature for mobile
WEAKLY SUPERVISED TOPIC GROUPING OF YOUTUBE SEARCH RESULTS
"... Recent years have witnessed an explosive growth of user contributed videos on websites like YouTube and Metacafe, which usually provide a query-by-keyword functionality to facilitate the user browsing. For a given query, the returned videos typically contain multiple topics that are mixed up to dupl ..."
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Recent years have witnessed an explosive growth of user contributed videos on websites like YouTube and Metacafe, which usually provide a query-by-keyword functionality to facilitate the user browsing. For a given query, the returned videos typically contain multiple topics that are mixed up to duplicate the user browsing. Therefore, their diversifica-tion and grouping are highly demanded to improve the user experiences. However, the tagging and content qualities of user contributed videos are uncontrolled against their pre-cise grouping. In this paper, we present a weakly supervised topic grouping paradigm to diversify the returned videos of a given keyword query. Our grouping is based on the bag-of-words visual signature quantized over the spatiotemporal STIP descriptor [1] extracted from each returned video. First, we adopt a min-Hashing based visual similarity in combina-tion of the tagging similarity to group the returned videos. Based on the initial grouping configurations, we mine the co-occurred discriminative sub-signatures, based on which we iteratively refine the first step. Such iteration well handles the noise in visual content and tagging, since neither of which is fully trusted during the grouping. We validate our schemes on over 2,000 video clips crawled from a set of YouTube keyword query results. Comparing to alternative approaches, our scheme has shown superior robustness and precision.
a r t i c l e i n f o Article history:
, 2014
"... Keywords: Web video analysis a b s t r a c t also becoming an emerging important research direction [27,26,25,28,9]. recently a nition, visu guide, geography-aware image search, mobile device localization, and aiding virtual reality. Geographically tagging user-contributed images has been investigat ..."
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Keywords: Web video analysis a b s t r a c t also becoming an emerging important research direction [27,26,25,28,9]. recently a nition, visu guide, geography-aware image search, mobile device localization, and aiding virtual reality. Geographically tagging user-contributed images has been investigated in recent years [16,13]. However, tagging w eos has been less explored so far, in which inferring geo-locations of web videos is especially vital to location-based services. The key difficulty of video geo-location inference is twofold. On the device level, current mobile phones and digital
A System for Retrieval Mobile Document Images Efficiently from Digital Library
"... Abstract — Recently mobile devices are used to improve the user experience of digital library browsing and search. Various applications such as education, product retrieval and location search. Searching the document and transferring the query are important issues in mobile document search. In this ..."
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Abstract — Recently mobile devices are used to improve the user experience of digital library browsing and search. Various applications such as education, product retrieval and location search. Searching the document and transferring the query are important issues in mobile document search. In this paper, provides a model for automatically generating a caption for images with a mobile document image retrieval framework. The proposed system, consist of three stages: Content Selection, Surface realization and document retrieval. Here author considering both extractive and abstractive model and discuss. A Hamming distance KD-Tree is used for scalable searching. A JBIG2 based query image compression to fulfill the low bit rate query.
Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation
"... The task of a visual landmark recognition system is to identify photographed buildings or objects in query photos and to provide the user with relevant information on them. With their increasing coverage of the world’s landmark buildings and objects, Internet photo collections are now being used as ..."
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The task of a visual landmark recognition system is to identify photographed buildings or objects in query photos and to provide the user with relevant information on them. With their increasing coverage of the world’s landmark buildings and objects, Internet photo collections are now being used as a source for building such systems in a fully automatic fashion. This process typically consists of three steps: clustering large amounts of images by the objects they depict; determining object names from user-provided tags; and building a robust, compact, and efficient recog-nition index. To this date, however, there is little empirical information on how well current approaches for those steps perform in a large-scale open-set mining and recognition task. Furthermore, there is little empirical information on how recognition performance varies for different types of landmark objects and where there is still potential for improvement. With this paper, we intend to fill these gaps. Using a dataset of 500k images from Paris, we analyze each component of the landmark recognition pipeline in order to answer the following questions: How many and what kinds of objects can be discovered automatically? How can we best use the resulting image clusters to recognize the object in a query? How can the object be efficiently represented in memory for recognition? How reliably can semantic information be extracted? And finally: What are the limiting factors in the resulting pipeline from query to semantics? We evaluate how different choices of methods and parameters for the individual pipeline steps affect overall system performance and examine their effects for different query categories such as buildings, paintings or sculptures.
Realistic Real-Time Outdoor Rendering in Augmented Reality
, 2014
"... Realistic rendering techniques of outdoor Augmented Reality (AR) has been an attractive topic since the last two decades considering the sizeable amount of publications in computer graphics. Realistic virtual objects in outdoor rendering AR systems require sophisticated effects such as: shadows, day ..."
Abstract
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Realistic rendering techniques of outdoor Augmented Reality (AR) has been an attractive topic since the last two decades considering the sizeable amount of publications in computer graphics. Realistic virtual objects in outdoor rendering AR systems require sophisticated effects such as: shadows, daylight and interactions between sky colours and virtual as well as real objects. A few realistic rendering techniques have been designed to overcome this obstacle, most of which are related to non real-time rendering. However, the problem still remains, especially in outdoor rendering. This paper proposed a much newer, unique technique to achieve realistic real-time outdoor rendering, while taking into account the interaction between sky colours and objects in AR systems with respect to shadows in any specific location, date and time. This approach involves three main phases, which cover different outdoor AR rendering requirements. Firstly, sky colour was generated with respect to the position of the sun. Second step involves the shadow generation algorithm, Z-Partitioning: Gaussian and Fog Shadow Maps (Z-GaF Shadow Maps). Lastly, a technique to integrate sky colours and shadows through its effects on virtual objects in the AR system, is introduced. The experimental results reveal that the proposed technique has