Rank-SIFT: Learning to rank repeatable local interest points
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Abstract:
Scale-invariant feature transform (SIFT) has been well studied in recent years. Most related research efforts focused on designing and learning effective descriptors to characterize a local interest point. However, how to identify stable local interest points is still a very challenging problem. In this paper, we propose a set of differential features, and based on them we adopt a data-driven approach to learn a ranking function to sort local interest points according to their stabilities across images containing the same visual objects. Compared with the handcrafted rule-based method used by the standard SIFT algorithm, our algorithm substantially improves the stability of detected local interest point on a very challenging benchmark dataset, in which images were generated under very different imaging conditions. Experimental results on the Oxford and PASCAL databases further demonstrate the superior performance of the proposed algorithm on both object image retrieval and category recognition.Keywords:
Rank (graph theory)
Scale-invariant feature transform
Learning to Rank
In Recent years, the application of machine learning approaches to conventional IR system evolve a new dimension in the field. The emphasis is now shifted from simply retrieving a set of documents to rank them also for a given query in terms of user's need. The researcher's task is not only to retrieve the documents from the corpus but also to rank them in order of their relevance to the user's requirement. To improve the system's performance is now the hot area of research. In this paper, an attempt has been made to put some of most commonly used algorithms in the community. It presents a survey on the approaches used to rank the retrieved documents and their evaluation strategies.
Rank (graph theory)
Relevance
Learning to Rank
Mean reciprocal rank
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C-Rank (contribution-based ranking) is a state-of-the-art algorithm for ranking web pages. It combines content and link information by introducing the concept of contribution, which implies how much a page contributes to improving the content quality of other pages. However, C-Rank suffers from taking infeasible time to reflect changes in World Wide Web to every page's C-Rank score. In this paper, we propose an effective and efficient method to incrementally maintain C-Rank scores of terms in web pages. Our proposed method is carefully designed to selectively update C-Rank scores of specific pages without any accuracy loss, rather than re-computing all the C-Rank scores of all the terms in all the pages from the scratch. Our experimental results on real-world dataset confirm the effectiveness and efficiency of our proposed method.
Rank (graph theory)
Learning to Rank
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With the aim to solve the implement problem in scale invariant feature transform (SIFT) algorithm, the theory and the implementation process was analyzed in detail. The characteristics of the SIFT method were analyzed by theory, combined with the explanation of the Rob Hess SIFT source codes. The effect of the SIFT method was validated by matching two different real images. The matching result shows that the features extracted by SIFT method have excellent adaptive and accurate characteristics to image scale, viewpoint change, which are useful for the fields of image recognition, image reconstruction, etc.
Scale-invariant feature transform
Image Matching
Feature Matching
Feature (linguistics)
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An efficient technique to find the rank of the research papers based on the author name of numerous research fields published in several conferences. This ranking process is based on the citation network. Research paper vital is caught well with through associate vote, which on this case is exploration paper being referred to in various research papers. Utilizing an adjusted adaptation of the Page Rank calculation, we rank the exploration papers, relegating everything about a definitive positioning. Utilizing the scores of the exploration papers figured by the utilization of aforementioned procedure, we plan scores for the creators and rank them as great. We have introduced a crawler based dynamic system in algorithm which receive into account access time, number of citations and author details in ranking the research papers. Considering the papers publication, in the addition to paper scores we compare the page rankings of the research links calculated for the authors.
Rank (graph theory)
Web crawler
Learning to Rank
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Rank (graph theory)
Learning to Rank
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This paper summarizes the three robust feature detection and matching methods: Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFT and Speeded Up Robust Features (SURF). SIFT find its interest points using Difference of Gaussian (DoG). SIFT presents its stability in most situations although it’s slow. PCA-SIFT show its advantages in rotation and illumination change and it is faster than SIFT.SURF is the fastest one with good performance as the same as SIFT. ‘Fast-Hessian’ detector that used in SURF is more than 3 times faster that DOG.
Scale-invariant feature transform
Hessian matrix
Feature (linguistics)
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Numerous studies have been focusing on the improvement of bag of features (BOF), histogram of oriented gradient (HOG) and scale invariant feature transform (SIFT). However, few works have attempted to learn the connection between them even though the latter two are widely used as local feature descriptor for the former one. Motivated by the resemblance between BOF and HOG/SIFT in the descriptor construction, we improve the performance of HOG/SIFT by a) interpreting HOG/SIFT as a variant of BOF in descriptor construction, and then b) introducing recently proposed approaches of BOF such as locality preservation, data-driven vocabulary, and spatial information preservation into the descriptor construction of HOG/SIFT, which yields the BOF-driven HOG/SIFT. Experimental results show that the BOF-driven HOG/SIFT outperform the original ones in pedestrian detection (for HOG), scene matching and image classification (for SIFT). Our proposed BOF-driven HOG/SIFT can be easily applied as replacements of the original HOG/SIFT in current systems since they are generalized versions of the original ones.
Scale-invariant feature transform
Feature (linguistics)
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The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the core and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions.
Rank (graph theory)
Learning to Rank
Mean reciprocal rank
Competitor analysis
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SIFT(scale invariant feature transform)는 크기와 회전에 불변하는 특징을 추출하는 방법으로, 다양한 검출 및 인식 분야에 적용되고 있다. 이러한 특성으로 인하여 SIFT는 복사-이동 조작 검출을 위한 기본 변환으로 널리 사용되고 있다. 그러나 SIFT 기반 복사-이동 조작 검출 방법은 배경 영역이 조작된 경우, 조작 범위가 작은 경우, 영상이 압축된 경우 등에 검출 성능이 떨어지는 단점을 가지고 있다. 본 논문에서는 이러한 단점을 극복하기 위하여 CSLBP(center-symmetric local binary pattern) 지시자를 이용한 SIFT 기반 복사-이동 조작 검출법을 제안한다. CSLBP 지시자는 총 16차원을 가지며, 기존의 128 차원의 SIFT 지시자에 추가하여 사용된다. MICC-F220 및 CMH 데이터에 대한 실험 결과, 제안 방법은 95% 이상의 검출 정확도를 보인다. 특히, 압축된 영상에 대하여 성능의 저하가 없음을 알 수 있다.
Scale-invariant feature transform
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