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2nd International Congress on image and Signal Processing

作者： Li, Daxiang Peng, Jinye NW Univ Xian Sch Informat Technol Xian 710069 Peoples R China

ISBN: (纸本)9781424441303

Aiming at the problem of object-based image retrieval, a novel semi-supervised multi-instance learning (MIL) algorithm based on RS (rough set) attribute reduction and Transductive support vector machine (TSVM) has been presentedw-RSTSVM-MIL algorithm. This algorithm regards the whole image as a bag, and the low-level visual feature of the segmented regions as instances, in order to transform every bag to a single sample, RSTSVM-MIL first generates a collection of "visual-word" to construct projection space by K-means clustering method in the instance feature space, then a nonlinear mapping is defined using these "visual-word" and maps every bag to a point in the projection space, extracting all bags' projection feature, which converts MIL problem to a standard supervised learning problem. Finally, in order to improve the efficiency of classifier training and to use a large amount of unlabeled image together with the labeled image to build better classifiers, we use RS (rough set) method to reduce the redundant information in projection feature firstly, then semi-supervised learning algorithm TSVM be used to train classifier. Experimental results on the SIVAL image set show that solving MIL problems using semi-supervised learning is feasible, and the RSTSVM-MIL is competitive with other state-of-the-art MIL algorithms in object-based image retrieval problem.

关键词： multi-instance learning object-based image retrieval attribute reduction

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object-based image retrieval with Attention Analysis and Spatial Re-ranking

Object-based Image Retrieval with Attention Analysis and Spa...

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5th IFIP International Conference on Intelligent Information Processing

作者： Gao, Ke Lin, Shouxun Zhang, Yongdong Tang, Sheng Grad Univ Chinese Acad Sci Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100080 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proces Beijing 100080 Peoples R China

ISBN: (纸本)9780387876849

In this paper, a new method is proposed for object-based image retrieval. The user supplies a query object by selecting a region from a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large image database. The main outcomes of this research are as follows: (1) An novel object-based image retrieval framework that integrates effective pre-treatment and re-ranking is presented, (2) a new feature filtration method based on attention analysis is proposed for pre-treatment, (3) to further improve object retrieval precision, we add an efficient spatial configuration model to re-rank the primary retrieval result using Bag of Word method. Experimental results demonstrate the effectiveness of our method.

关键词： object-based image retrieval attention analysis spatial re-ranking

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Toward a higher-level visual representation for object-based image retrieval

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VISUAL COMPUTER 2009年第1期25卷 13-23页

作者： Zheng, Yan-Tao Neo, Shi-Yong Chua, Tat-Seng Tian, Qi Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Singapore 117548 Singapore Natl Univ Singapore Dept Comp Sci Singapore 117548 Singapore Natl Univ Singapore Sch Comp Singapore 117548 Singapore Inst Infocomm Res Singapore Singapore

We propose a higher-level visual representation, visual synset, for object-based image retrieval beyond visual appearances. The proposed visual representation improves the traditional part-based bag-of-words image representation, in two aspects. First, the approach strengthens the discrimination power of visual words by constructing an intermediate descriptor, visual phrase, from frequently co-occurring visual word-set. Second, to bridge the visual appearance difference or to achieve better intra-class invariance power, the approach clusters visual words and phrases into visual synset, based on their class probability distribution. The rationale is that the distribution of visual word or phrase tends to peak around its belonging object classes. The testing on Caltech-256 data set shows that the visual synset can partially bridge visual differences of images of the same class and deliver satisfactory retrieval of relevant images with different visual appearances.

关键词： Visual representation object-based image retrieval More

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Constructing Visual Phrases for Effective and Efficient object-based image retrieval

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2008年第1期5卷 1–19页

作者： Zheng, Qing-Fang Gao, Wen Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China

The explosion of multimedia data necessitates effective and efficient ways for us to get access to our desired ones. In this article, we draw an analogy between image retrieval and text retrieval and propose a visual phrase-based approach to retrieve images containing desired objects (object-based image retrieval). The visual phrase is defined as a pair of frequently co-occurred adjacent local image patches and is constructed using data mining. We design methods on how to construct visual phrase and how to index/search images based on visual phrase. We demonstrate experiments to show our visual phrase-based approach can be very efficient and more effective than current visual word-based approach.

关键词： Algorithms Experimentation Performance Content-based image retrieval inverted index local image descriptor SIFT object-based image retrieval visual phrase

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object-based image retrieval with Kernel on Adjacency Matrix and Local Combined Features

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2012年第4期8卷 54-54页

作者： Qi, Heng Li, Keqiu Shen, Yanming Qu, Wenyu Dalian Univ Technol Sch Elect & Informat Engn Dalian 116024 Peoples R China Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116023 Peoples R China

In object-based image retrieval, there are two important issues: an effective image representation method for representing image content and an effective image classification method for processing user feedback to find more images containing the user-desired object categories. In the image representation method, the local-based representation is the best selection for object-based image retrieval. As a kernel-based classification method, Support Vector Machine (SVM) has shown impressive performance on image classification. But SVM cannot work on the local-based representation unless there is an appropriate kernel. To address this problem, some representative kernels are proposed in literatures. However, these kernels cannot work effectively in object-based image retrieval due to ignoring the spatial context and the combination of local features. In this article, we present Adjacent Matrix (AM) and the Local Combined Features (LCF) to incorporate the spatial context and the combination of local features into the kernel. We propose the AM-LCF feature vector to represent image content and the AM-LCF kernel to measure the similarities between AM-LCF feature vectors. According to the detailed analysis, we show that the proposed kernel can overcome the deficiencies of existing kernels. Moreover, we evaluate the proposed kernel through experiments of object-based image retrieval on two public image sets. The experimental results show that the performance of object-based image retrieval can be improved by the proposed kernel.

关键词： Algorithms Experimentation Performance object-based image retrieval feedback processing kernel local combined features

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A KFD web database system with an object-based image retrieval for family art therapy assessments

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ARTS IN PSYCHOTHERAPY 2010年第3期37卷 163-171页

作者： Im, Y. H. Oh, S. G. Chung, M. J. Yu, J. H. Lee, H. S. Chang, J. K. Park, D. H. Korea Univ Coll Sci & Technol Dept Comp & Informat Sci Yeongi Gun 339700 Chungnam South Korea Elect & Telecommun Res Inst Taejon 305606 South Korea Sookmyung Womens Univ Coll Human Ecol Dept Family Resources Management Seoul South Korea

In this paper, we apply the DataBase Management System (DBMS) to Kinetic Family Drawing (KFD), which is a widely used assessment tool in family art therapy. We developed a prototype of the KFD Web database for managing large amounts of KFD assessment data, and supporting an efficient KFD assessment process by analyzing the requirements of family art therapists. We also devised and implemented a novel object-based image retrieval algorithm by using high-level features that are recognizable by humans in the context of the KFD Web database system. Consequently, a family art therapist can more consistently and objectively assess family problems by referencing the existing assessment content that was obtained with object-based image retrieval and a systematic knowledge management system in the KFD Web database system. We explain the proposed system's functions, and illustrate using case studies of object-based image retrieval. Our proposed system can be used as a useful supplementary tool, and it will aid humans in KFD sessions. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Family art therapy assessments Kinetic Family Drawing object-based image retrieval Database management system

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MULTI-GRAPH MULTI-INSTANCE LEARNING WITH SOFT LABEL CONSISTENCY FOR object-based image retrieval

MULTI-GRAPH MULTI-INSTANCE LEARNING WITH SOFT LABEL CONSISTE...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Li, Fei Liu, Rujie Fujitsu Res & Dev Ctr Co Ltd Beijing 100025 Peoples R China

ISBN: (纸本)9781479970827

object-based image retrieval has been an active research topic in the last decade, in which a user is only interested in some object instead of the whole image As a promising approach, graph-based multi-instance learning has been paid much attention. Early retrieval methods often conduct learning on one graph in either image or region level. To further improve the performance, some recent methods adopt multi-graph learning, but the relationship between image- and region-level information is not well explored. In this paper, by constructing both image- and region-level graphs, a novel multi-graph multi-instance learning method is proposed. Different from the existing methods, the relationship between each labeled image and its segmented regions is reflected by the consistency of their corresponding soft labels, and it is formulated by the mutual restrictions in an optimization framework. A comprehensive cost function is designed to involve all the available information, and an iterative solution is introduced to solve the problem. Experimental results on the benchmark data set demonstrate the effectiveness of our proposal.

关键词： object-based image retrieval graph-based learning multi-instance learning

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Integrated global and object-based image retrieval using a multiple example query scheme

Integrated global and object-based image retrieval using a m...

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2nd International Conference on Computer Graphics Theory and Applications/2nd International Conference on Computer Vision Theory and Applications

作者： Borba, Gustavo B. Gamba, Humberto R. Mayron, Liam M. Marques, Oge Univ Tecnol Fed Parana CPGEI Av Sete Setembro 3165 Curitiba Parana Brazil Florida Aslamic Univ Dept Comp Sci & Engn Boca Raton FL 33431 USA

ISBN: (纸本)9789728865740

Conventional content-based image retrieval (CBIR) systems typically do not consider the limitations of the feature extraction-distance measurement paradigm when capturing a user's query. This issue is compounded by the complicated interfaces that are featured by many CBIR systems. The framework proposed in this work embodies new concepts that help mitigate such limitations. The front-end includes an intuitive user interface that allows for fast image organization though spatial placement and scaling. Additionally, a multiple-image query is combined with a region-of-interest extraction algorithm to automatically trigger global or object-based image analysis. The relative scale of the example images are considered to be indicative of image relevance and are also considered during the retrieval process. Experimental results demonstrate promising results.

关键词： content-based image retrieval object-based image retrieval multiple example query ROI extraction

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Classifying images using multiple binary-class decision trees for object-based image retrieval

Classifying images using multiple binary-class decision tree...

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2nd Conference on Internet Imaging

作者： Jia, LH Kitchen, L Univ Melbourne Dept Comp Sci & Software Engn Comp Vis & Machine Intelligence Lab Parkville Vic 3052 Australia

ISBN: (纸本)0819439894

This paper describes an approach to multiclass object classification using local information based invariant object-contour representation and a combination of one-per-class binary-class decision tree classifiers. The object representation scheme is based on the polygonal approximations of object contours. C4.5 is used to learn each of the binary-class tree classifiers which are used to predict the class of each segment of an object. A new decision combination method is used to determine the class of an object based on class probability distribution of each segment of the object on each of the binary-class trees. The proposed object classification approach is invariant to translation, rotation, and scale changes of objects. On applying this approach to a hand tool image database in the situation of image retrieval, tile experimental results show that the retrieval performance is significantly better than the results obtained by previous studies.

关键词： object invariant representation object classification decision tree classifiers object-based image retrieval

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Multi-SVM Multi-instance Learning for object-based image retrieval

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15th International Conference on Computer Analysis of images and Patterns (CAIP)

作者： Li, Fei Liu, Rujie Baba, Takayuki Fujitsu Res & Dev Ctr Co Ltd Beijing Peoples R China Fujitsu Labs Ltd Kawasaki Kanagawa Japan

ISBN: (纸本)9783642402616

object-based image retrieval has been an active research topic in recent years, in which a user is only interested in some object in the images. The recently proposed methods try to comprehensively use both image-and region-level features for more satisfactory performance, but they either cannot well explore the relationship between the two kinds of features or lead to heavy computational load. In this paper, by adopting support vector machine (SVM) as the basic classifier, a novel multi-instance learning method is proposed. To deal with the different forms of image-and region-level representations, standard SVM and multi-instance SVM are utilized respectively. Moreover, the relationship between images and their segmented regions is also taken into account. A unified optimization framework is developed to involve all the available information, and an efficient iterative solution is introduced. Experimental results on the benchmark data set demonstrate the effectiveness of our proposal.

关键词： object-based image retrieval support vector machine multi-instance learning

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