AHP: A New Strategy for the Semantic Concept Detection in Video

Video semantic concept detection is considered as an important research problem by the multimedia industry in recent years. Classification is the most accepted method used for concept detection, where, the output of the classification system is interpreted as semantic concepts. These concepts can be employed for automatic indexing, searching and retrieval of video objects. However, employed features have high dimensions and thus, concept detection with the existing classifiers experiences high computation complexity. In this paper, a new approach is proposed to reduce the classification complexity and the required time for learning and classification by choosing the most important features. For this purpose genetic algorithms are employed as a feature selector. Simulation results illustrate improvements in the behavior of the classifier.

Recent research has discovered that leveraging ontology is an effective way to facilitate semantic video concept detection. As an explicit knowledge representation, a formal ontology definition usually consists of a lexicon, properties, and relations. In this paper, we present a comprehensive representation scheme for video semantic ontology in which all the three components are well studied. Specifically, we leverage LSCOM to construct the concept lexicon, describe concept property as the weights of different modalities which are obtained manually or by data-driven approach, and model two types of concept relations (i.e., pairwise concept correlation and hierarchical relation). In contrast with most existing ontologies which are only focused on one or two components for domain-specific videos, the proposed ontology is more comprehensive and general. To validate the effectiveness of this ontology, we further apply it to video concept detection. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared to existing key approaches to video concept detection.

Multi-level feature representations for video semantic concept detection

In this paper a discriminant analysis (DA) technique called accelerated generalised subclass discriminant analysis (AGSDA) and its GPU implementation are presented. This method identifies a discriminant subspace of the input space in three steps: a) Gram matrix computation, b) eigenvalue decomposition of the between subclass factor matrix, and c) computation of the solution of a linear matrix system with symmetric positive semidefinite (SPSD) matrix of coefficients. Based on the fact that the computationally intensive parts of AGSDA, i.e. Gram matrix computation and identification of the SPSD linear matrix system solution, are highly parallelisable, a GPU implementation of AGSDA is proposed. Experimental results on large-scale datasets of TRECVID for event and concept detection show that our GPU-AGSDA method combined with LSVM outperforms LSVM alone in training time, memory consumption, and detection accuracy.

Single visual concept detection in videos is a hard task, especially for infrequent concepts or for those difficult to model. This question becomes even more difficult in the case of concept pairs. Two main directions may tackle this problem: 1) combine the predictions of their corresponding detectors in a way which is similar to usual information retrieval, or 2) build supervised learners for these pairs of concepts by generating annotations based on the occurrences of the two individual concepts. Each of these approaches have advantages and drawbacks. We evaluated them in the context of the concept pair detection subtask of the TRECVid 2013 semantic indexing (SIN) task and found that information retrieval-like fusions of concept detection scores outperforms the learning approaches. The described methods outperform the best official result of the evaluation campaign cited previously, by 9% in terms of relative improvement on MAP.

We present a probabilistic ranking-driven classifier for the detection of video semantic concept, such as airplane, building, etc. Most existing concept detection systems utilize Support Vector Machines (SVM) to perform the detection and ranking of retrieved video shots. However, the margin maximization principle of SVM does not perform ranking optimization but merely classification error minimization. To tackle this problem, we exploit the sparse Bayesian kernel model, namely the relevance vector machine (RVM), as the classifier for semantic concept detection. Based on automatic relevance determination principle, RVM outputs the posterior probabilistic prediction of the semantic concepts. This inference output is optimal for ranking the target video shots, according to the Probabilistic Ranking Principle. The probability output of RVM on individual uni-modal features also facilitates probabilistic fusion of multi-modal evidences to minimize Bayes risk. We demonstrate both theoretically and empirically that RVM outperforms SVM for video semantic concept detection. The testings on TRECVID 07 dataset show that RVM produces statically significant improvements in MAP scores over the SVM-based methods.

In this paper, we propose a novel semi-supervised feature analyzing framework for multimedia data understanding and apply it to three different applications: image annotation, video concept detection and 3-D motion data analysis. Our method is built upon two advancements of the state of the art: (1) <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2, 1</sub> -norm regularized feature selection which can jointly select the most relevant features from all the data points. This feature selection approach was shown to be robust and efficient in literature as it considers the correlation between different features jointly when conducting feature selection; (2) manifold learning which analyzes the feature space by exploiting both labeled and unlabeled data. It is a widely used technique to extend many algorithms to semi-supervised scenarios for its capability of leveraging the manifold structure of multimedia data. The proposed method is able to learn a classifier for different applications by selecting the discriminating features closely related to the semantic concepts. The objective function of our method is non-smooth and difficult to solve, so we design an efficient iterative algorithm with fast convergence, thus making it applicable to practical applications. Extensive experiments on image annotation, video concept detection and 3-D motion data analysis are performed on different real-world data sets to demonstrate the effectiveness of our algorithm.

With the rapid growth of multimedia application technologies and network technologies, especially the proliferation of Web 2.0 and digital cameras, there has been an explosion of images and videos in the Internet. For example, the volume of videos uploaded to the YouTube every minute is amounting to 48 hours by May 2011, having doubled in the last two years. Such huge video collections hold useful yet implicit and nontrivial knowledge about various domains. To manage and utilize these resources effectively, video concept detection becomes a very important subject of intensive research by a large research community (Over et al., 2008). It is an integral part of visual data mining that is automatically extracting such knowledge from the huge unstructured visual data. It aims to automatically annotate video shots or keyframes with respect to a semantic concept (Tang et al., 2012). Ranging from objects like airplane and car to scenes like urban street and sky, semantic concepts serve as good intermediate semantic features for video content indexing and understanding, and thus, spurring much research attention (Jiang et al., 2010; Naphade & Smith, 2004; Snoek et al., 2006; Zheng et al., 2008). Essentially, concept detection is a classification task, in which a binary classifier is usually learned to predict the presence of a certain concept in a video shot or keyframe (image). Traditional concept detection methods are mainly global classification: use supervised machine learning techniques, such as single Support Vector Machine (SVM), etc., over whole training dataset.

Media mining, the extraction of meaningful knowledge from multimedia content has become a major application and poses significant computational challenges in today's platforms. Media mining applications contain many sophisticated algorithms that include data-intensive analysis, classification, and learning. This paper explores the use of Graphics Processing Units (GPU) in media mining. We are particularly focused on large-scale semantic concept detection, a state-of-the-art approach that maps media content to hight-level semantic concepts, and a building block in many Media mining applications. We present a fast, parallel, large-scale, high-level semantic concept detector that leverages the GPU for image/video retrieval and content analysis. Through efficient data partitioning and movement, we parallelize feature extraction routines. By interleaving feature extraction routines of different types, we increase the computational intensity and mitigate the negative effects of histogram-like reduction operations. To cope with the very large number of semantic concepts, we propose a data layout of concept models on a multi-GPU hybrid architecture for high throughput semantic concept detection. We achieve one to two orders of magnitude speedups compared to serial implementations and our experiments show that we can detect 374 semantic concepts at a rate of over 100 frames/sec. This is over 100 times faster than a LibSVM-based semantic concept detection.

The Analytic Hierarchy Process (AHP) is a powerful decision-making tool for handling complex, multi-criteria situations. Developed by Thomas L. Saaty in the 1970s, AHP structures problems into a hierarchical format, enabling the systematic evaluation of alternatives based on multiple criteria through pairwise comparisons and numerical scales. Consequently, this chapter explores AHP’s methodology, practical applications, and implementation strategies. It provides a detailed guide on the AHP process, from problem definition and hierarchy structuring to conducting pairwise comparisons, calculating priorities, and synthesizing results. The chapter includes diverse case studies illustrating AHP’s utility in domains such as VMCPU frequency selection for task scheduling, project selection, supply chain management, strategic planning, environmental sustainability, and healthcare. Practical implementation advice covers structuring decision hierarchies, ensuring consistency in pairwise comparisons, performing sensitivity analyses, and addressing challenges such as subjective judgments and data quality issues. The chapter concludes with recommendations for effectively applying AHP, highlighting the importance of stakeholder involvement, transparency, and integrating AHP with other decision-making tools. Emphasizing the flexibility and broad applicability of AHP, the chapter underscores its value in facilitating informed and transparent decision-making in various contexts.

PurposeThe purpose of this paper is to show that there is a wealth of academic literature that qualitatively examines the outsourcing and offshoring from a go/no go perspective. The paper examines the complex “where to outsource” question by applying a quantitative approach called Analytic Hierarchy Process (AHP).Design/methodology/approachThe authors examine a Fortune 500 company's real‐world decision about where to outsource and summarize the current selection process employed by the company. We then apply our AHP model to the company's decision and compare the results.FindingsThere are four major findings: the location selection decision is a component of the outsource supplier selection decision; the AHP model effectively manages the complexity of the decision making process, incorporating all decision criteria harmoniously; a method such as AHP, which is able to incorporate both qualitative and quantitative criteria into evaluations, would streamline the decision‐making process; and the AHP process allows firms to look at a portfolio of choices and determine which firms are basically equal in qualifications.Research limitations/implicationsThe research implies that: (AHP may be more applicable in these areas by providing a rigorous framework for assessment of qualitative and quantitative factors together; and AHP offers substantial flexibility to accommodate the variety and quantity of decision criteria set forth by the firm.Practical implicationsAs firms are more active in pursuing opportunities in global markets, identifying the right offshoring location is critical. The selection process is complex, involving a set of qualitative and quantitative factors and requiring rigorous and careful analysis. Therefore, a scientific method that not only offers flexibility and simplicity, but also simultaneously accommodates a wide variety of decision criteria is invaluable. The research demonstrates that AHP provides these benefits and is an effective technique for analyzing the where‐to‐outsource decision.Originality/valueThe extensive literature review suggests that the majority of the existing works focus on analyzing the go/no‐go decision using a qualitative approach. This paper applies the AHP method to the “where to outsource” question to demonstrate one quantitative approach to this complex decision. Additionally, the paper provides a detailed description of how the AHP method is implemented in analyzing the decision by using a Fortune 500 company's data and information as an example.

Cross-domain video concept detection: A joint discriminative and generative active learning approach

Transductive video concept detection is an effective way to handle the lack of sufficient labeled videos. However, another issue, the multi-label interdependence, is not essentially addressed in the existing transductive methods. Most solutions only applied the transductive single-label approach to detect each individual concept separately, but ignoring the concept relation, or simply imposed the smoothness assumption over the multiple labels for each video, without indeed exploring the interdependence between the concepts. On the other hand, the semi-supervised extension of supervised multi-label classifiers, such as correlative multi-label support vector machines, is usually intractable and hence impractical due to the quite expensive computational cost. In this paper, we propose an effective transductive multi-label classification approach, which simultaneously models the labeling consistency between the visually similar videos and the multi-label interdependence for each video in an integrated framework. We compare the performance between the proposed approach and several representative transductive single-label and supervised multi-label classification approaches for the video concept detection task over the widely-used TRECVID data set. The comparative results demonstrate the superiority of the proposed approach.

Normally, statistical methods are used to generate rankings for genes in terms of their ability to distinguish between normal and malignant tumors from a gene expression dataset. However, different statistical methods yield different ranks for same gene and there is no universally accepted method for ranking. Therefore rank aggregation is required to find the overall ranking of the set of genes. There are various rank aggregation methods in the existing literature to integrate the rankings produced by various statistical tests. Moreover, the problem of integration of some partial rankings, containing unequal numbers of genes, is more challenging. In this article, a multiobjective genetic algorithm based rank aggregation method is proposed to integrate some partial rankings in an unbiased way. The first objective is to minimize the total distance from the reference ranking to the input rankings. For distance calculation, the Scaled Footrule Distance is used. The second objective is to minimize the standard deviation among those distances in order to avoid bias toward a particular input ranking. The proposed method is applied on some real-life microarray gene expression datasets, and the performance of it is compared with that of several existing rank aggregation techniques with respect to accuracy and the AUC (Area under ROC curve) value. Again, for real-life datasets, accuracy is plotted for visual comparison.

This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems.