Comparing discriminating transformations and SVM for learning during multimedia retrieval

Zhou, Xiaoping; Huang, Thomas S.

doi:10.1145/500160.500163

Cited by 11 publications

(19 citation statements)

References 10 publications

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“…Images similar to the query images can be similar in differing features. (This observation runs counter to the assumption recently made in [34], that similar images are similar in the same way. )…”

Section: Visual Data Mining Resultscontrasting

confidence: 87%

DynDex

Goh

Chang

2002

Proceedings of the Tenth ACM International Conference on Multimedia

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To date, almost all research work in the Content-Based Image Retrieval (CBIR) community has used Minkowski-like functions to measure similarity between images. In this paper, we first present a non-metric distance function, dynamic partial function (DPF), which works significantly better than Minkowski-like functions for measuring perceptual similarity; and we explain DPF's link to similarity theories in cognitive science. We then propose DynDex, an indexing method that deals with both the dynamic and non-metric aspects of the distance function. DynDex employs statistical methods including distance-based classification and bagging to enable efficient indexing with DPF. In addition to its efficiency for conducting similarity searches in very highdimensional spaces, we show that DynDex remains effective when features are weighted dynamically for supporting personalized searches.

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Section: Visual Data Mining Resultscontrasting

confidence: 87%

DynDex

Goh

Chang

2002

Proceedings of the Tenth ACM International Conference on Multimedia

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“…Without a doubt, negative examples can provide valuable discriminative information. The question is how to model and utilize them in the right way [41]. (4) Developing more reasonable region weighting schemes.…”

Section: Discussionmentioning

confidence: 99%

“…In order to reduce this gap, two approaches have been widely used: regionbased features to represent the focus of the user's perceptions of image content [4,8,9,14,19,20,23,29,36,42] and relevance feedback (RF) to learn the user's intentions [2,13,18,21,28,32,34,39,41].…”

Section: Introductionmentioning

confidence: 99%

An effective region-based image retrieval framework

Feng¹,

Zhang

et al. 2002

Proceedings of the Tenth ACM International Conference on Multimedia

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We present a region-based image retrieval framework that integrates efficient region-based representation in terms of storage and retrieval and effective on-line learning capability. The framework consists of methods for image segmentation and grouping, indexing using modified inverted file, relevance feedback, and continuous learning. By exploiting a vector quantization method, a compact region-based image representation is achieved. Based on this representation, an indexing scheme similar to the inverted file technology is proposed. In addition, it supports relevance feedback based on the vector model with a weighting scheme. A continuous learning strategy is also proposed to enable the system to self improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.

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“…In the statistical machine learning approach, Support Vector Machine (SVM) [19] and the kernel discriminant analysis [2,20,21] are the two most popular approaches used to cluster non-linear data. Both approaches are essentially based on the kernel method to handle the non-linear data.…”

Section: B Support Vector Machine Vs Discriminant Analysismentioning

confidence: 99%

Reducing User Log size in an Inter-Query Learning Content Based Image Retrieval (CBIR) System with a Cluster Merging approach

Chung¹,

Wong²,

Fung³

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Use of relevance feedback (RF) in the feature vector model has been one of the most popular approaches to fine tune query for content-based image retrieval (CBIR) systems. This paper proposes a framework that extends the RF approach to capture the inter-query relationship between current and previous queries. By using the feature vector model, this approach avoids the need of "memorizing" actual retrieval relationship between the actual image indexes and the previous queries. This implies that the approach is more suitable for image database application where images are frequently added and removed. The proposed inter-query relationship is presented using a data cluster that is defined by a transformation matrix, a centroid point and the reference boundary value. These parameters are captured in a file commonly known as the user log. The file however will grow rapidly after successive retrieval sessions. In order to reduce the size of the user log, this paper introduces a merging approach to combine clusters that are close-by and similar in their characteristics. Experiments have shown that the proposed framework has out performed the short term learning approach and yet without the burden of the complex database maintenance strategies required in long-term learning approach.

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Comparing discriminating transformations and SVM for learning during multimedia retrieval

Cited by 11 publications

References 10 publications

DynDex

DynDex

An effective region-based image retrieval framework

Reducing User Log size in an Inter-Query Learning Content Based Image Retrieval (CBIR) System with a Cluster Merging approach

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