An automatic feature generation approach to multiple instance learning and its applications to image databases

Cheng, Hao; Hua, Kien A.; Yu, Ning

doi:10.1007/s11042-009-0335-3

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…They have performed experiment on COREL photo library. Cheng et al proposed BP-MLP and BP-SVM approaches for automatic image categorization in multiple instance learning scenario and performed experiment on 2000 images obtained from COREL repository [45]. They have extracted frequent patterns from each image category and embedded an image bag into a multidimensional data point which is useful to characterize the similarity between the image and every common pattern of an image category.…”

Section: Overview Of Multiple Instance Learning Approaches and Their mentioning

confidence: 99%

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Tomar

Agarwal

2016

Advances in Artificial Intelligence

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In multiple instance learning (MIL) framework, an object is represented by a set of instances referred to as bag. A positive class label is assigned to a bag if it contains at least one positive instance; otherwise a bag is labeled with negative class label. Therefore, the task of MIL is to learn a classifier at bag level rather than at instance level. Traditional supervised learning approaches cannot be applied directly in such kind of situation. In this study, we represent each bag by a vector of its dissimilarities to the other existing bags in the training dataset and propose a multiple instance learning based Twin Support Vector Machine (MIL-TWSVM) classifier. We have used different ways to represent the dissimilarity between two bags and performed a comparative analysis of them. The experimental results on ten benchmark MIL datasets demonstrate that the proposed MIL-TWSVM classifier is computationally inexpensive and competitive with state-of-the-art approaches. The significance of the experimental results has been tested by using Friedman statistic and Nemenyi post hoc tests.

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Section: Overview Of Multiple Instance Learning Approaches and Their mentioning

confidence: 99%

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Tomar

Agarwal

2016

Advances in Artificial Intelligence

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“…A feature vector based upon the object's appearance is also calculated. An object is represented as a bag of multiple instances [6,7], where each instance is a feature vector based upon an object's visual appearance at a point in the video stream. Therefore, an object can be viewed as a set of points in the multidimensional feature space, referred to as a point set (Fig.…”

Section: Tracking Based Upon a Multifaceted Object Modelmentioning

confidence: 99%

A general framework for managing and processing live video data with privacy protection

Aved

Hua

2011

Multimedia Systems

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Though a large body of existing work on video surveillance focuses on image and video processing techniques, few address the usability of such systems, and in particular privacy issues. This study fuses concepts from stream processing and content-based image retrieval to construct a privacy-preserving framework for rapid development and deployment of video surveillance applications. Privacy policies, instantiated to as privacy filters, may be applied both granularly and hierarchically. Privacy filters are granular as they are applicable to specific objects appearing in the video streams. They are hierarchal because they can be specified at specific objects in the framework (e.g., users, cameras) and are combined such that the disseminated video stream adheres to the most stringent aspect specified in the cascade of all privacy filters relevant to a video stream or query. To support this privacy framework, we extend our Live Video Database Model with an informatics-based approach to object recognition and tracking and add an intrinsic privacy model that provides a level of privacy protection not previously available for real-time streaming video data. The proposed framework also provides a formal approach to implement and enforce privacy policies that are verifiable, an important step towards privacy certification of video surveillance systems through a standardized privacy specification language.

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“…Data mining is a powerful new technology with great potential to help companies using data warehouses, it involves the use of data analysis tools to discover the valid patterns in a large data set. It entered to the all fields same as electronic commerce, insurance, medicine, management, engineering with a very fast speed (Ayanso and Yoogalingam, 2009;Rosset et al, 2010;Cheng et al, 2010;Zhou et al, 2010). Data mining could help the companies to increase their costumers and reduce the costs.…”

Section: Introductionmentioning

confidence: 99%

Developing a new suit sizing system using data optimization techniques

Esfandarani

Shahrabi

2012

Int Jnl of Clothing Sci & Tech

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Purpose -The purpose of this paper is to develop a new suit sizing system based on up-dated data, using data mining techniques, to improve the final quality and reduce the waste of fabric. This paper aims to investigate the effect of data reduction on the final fitness of the sizing chart. Design/methodology/approach -Principal component analysis is applied to reduce the sizing variables, non-hierarchical clustering approach is used to segment the heterogeneous population to more homogeneous one, and the aggregate loss of fitness is used to evaluate the resulted sizing chart. Findings -The results show that, when principal component analysis reduces the ten sizing variables to two main components, the final fitness for the resulted sizing chart is the best. These two main components are height and circumference. The hierarchical clustering approach could effectively group all body type to seven clusters. The resulted sizing chart could be used as a reference for suit manufacturers. Practical implications -Due to wide differences in race, nutrition and climate, people who live in different countries have their own body size; also, most of current sizing systems are out-dated, so there is an urgent need to develop a new sizing system. Due to the growing rate of globalization, the final results will be useful for those companies wanting to connect to global business chains. Originality/value -This work introduces the first suit sizing systems, based on data mining, for Iranian males, that has more fitness in comparison to the current sizing chart. The effect of the number of principal components on the final fitness of a sizing system is introduced as an innovative way, to avoid losing useful data during data reduction process with principal component analysis.

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An automatic feature generation approach to multiple instance learning and its applications to image databases

Cited by 6 publications

References 12 publications

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

A general framework for managing and processing live video data with privacy protection

Developing a new suit sizing system using data optimization techniques

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