Sparse Transfer Learning for Interactive Video Search Reranking

Visual reranking is effective to improve the performance of the text-based video search. However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low level visual features and high level semantic concepts. In this paper, we adopt interactive video search reranking to bridge the semantic gap by introducing user's labeling effort. We propose a novel dimension reduction tool, termed sparse transfer learning (STL), to effectively and efficiently encode user's labeling information. STL is particularly designed for interactive video search reranking. Technically, it a) considers the pair-wise discriminative information to maximally separate labeled query relevant samples from labeled query irrelevant ones, b) achieves a sparse representation for the subspace to encodes user's intention by applying the elastic net penalty, and c) propagates user's labeling information from labeled samples to unlabeled samples by using the data distribution knowledge. We conducted extensive experiments on the TRECVID 2005, 2006 and 2007 benchmark datasets and compared STL with popular dimension reduction algorithms. We report superior performance by using the proposed STL based interactive video search reranking.Comment: 17 page

A Review on Video Search Engine Ranking

Search reranking is considered as a best and basic approach to enhance recovery accuracy. The recordings are recovered utilizing the related literary data, for example, encompassing content from the website page. The execution of such frameworks basically depends on the importance between the content and the recordings. In any case, they may not generally coordinate all around ok, which causes boisterous positioning results. For example, outwardly comparative recordings may have altogether different positions. So reranking has been proposed to tackle the issue. Video reranking, as a compelling approach to enhance the consequences of electronic video look however the issue is not paltry particularly when we are thinking about different elements or modalities for pursuit in video and video recovery. This paper proposes another sort of reranking calculation, the round reranking, that backings the common trade of data over numerous modalities for enhancing seek execution and takes after the rationality of solid performing methodology could gain from weaker ones

A Survey on Video Recommendation and Ranking in Video Search Engine

This paper presents a recommender framework which has been created to study examination addresses in the field of news feature suggestion and personalization. The framework is focused around semantically advanced feature information and can be seen as a sample framework that permits look into on semantic models for versatile intelligent frameworks. Feature recovery is possible by positioning the specimens as per their likelihood scores that were anticipated by classifiers. It is frequently conceivable to enhance the recovery execution by re-positioning the examples. In this paper, we proposed a re-positioning strategy that enhances the execution of semantic feature indexing and recovery, by re-assessing the scores of the shots by the homogeneity and the way of the feature they fit in with. Contrasted with past works, the proposed strategy gives a system to the re-positioning through the homogeneous circulation of feature shots content in a worldly arrangement. DOI: 10.17762/ijritcc2321-8169.15021

A Review on Attribute Based Image Search Reranking

Image search reranking is one of the effective approach to refine the text-based image search result. Text-based image retrieval suffers from essential problems that are lead to the incapability of the associated text to appropriately evoke the image content. In this paper, reranking methods are put forward to address this drawback in scalable fashion. Based on the classifiers for each and every predefined attributes,each and every  image is represented by an attribute feature consisting of the responses from these classifiers. This hypergraph can be used to model the relationship between images by integration of low-level visual features and attribute features. Hypergraph ranking is then performed to order the images. Its basic principle is that visually close images should have identical ranking scores. It improves the performance over the text-based image search engin

Convexity and Operational Interpretation of the Quantum Information Bottleneck Function

In classical information theory, the information bottleneck method (IBM) can be regarded as a method of lossy data compression which focusses on preserving meaningful (or relevant) information. As such it has recently gained a lot of attention, primarily for its applications in machine learning and neural networks. A quantum analogue of the IBM has recently been defined, and an attempt at providing an operational interpretation of the so-called quantum IB function as an optimal rate of an information-theoretic task, has recently been made by Salek et al. However, the interpretation given in that paper has a couple of drawbacks; firstly its proof is based on a conjecture that the quantum IB function is convex, and secondly, the expression for the rate function involves certain entropic quantities which occur explicitly in the very definition of the underlying information-theoretic task, thus making the latter somewhat contrived. We overcome both of these drawbacks by first proving the convexity of the quantum IB function, and then giving an alternative operational interpretation of it as the optimal rate of a bona fide information-theoretic task, namely that of quantum source coding with quantum side information at the decoder, and relate the quantum IB function to the rate region of this task. We similarly show that the related privacy funnel function is convex (both in the classical and quantum case). However, we comment that it is unlikely that the quantum privacy funnel function can characterize the optimal asymptotic rate of an information theoretic task, since even its classical version lacks a certain additivity property which turns out to be essential.Comment: 17 pages, 7 figures; v2: improved presentation and explanations, one new figure; v3: Restructured manuscript. Theorem 2 has been found previously in work by Hsieh and Watanabe; it is now correctly attribute

Web Image re-ranking using Attribute Assisted Hypergraph

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