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INTEGRATING RELEVANCE FEEDBACK IN BOOSTING FOR CONTENT-BASED IMAGE RETRIEVAL
"... Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In this paper, we propose a novel interactive boosting framework to integrate user feedback into boosting scheme and bridge the ..."
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Cited by 4 (1 self)
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Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In this paper, we propose a novel interactive boosting framework to integrate user feedback into boosting scheme and bridge the gap between high-level semantic concept and low-level image features. Our method achieves more performance improvement from the relevance feedback than AdaBoost does because human judgment is accumulated iteratively to facilitate learning process. It also has obvious advantage over the classic relevance feedback method in that the classifiers are trained to pay more attention to wrongfully predicted samples in user feedback through a reinforcement training process. An interactive boosting scheme called i.Boost is implemented and tested using Adaptive Discriminant Projection (ADP) as base classifiers, which not only combines but also enhances a set of ADP classifiers into a more powerful one. To evaluate its performance, several applications are designed on UCI benchmark data sets, Harvard, UMIST, ATT facial image data sets and COREL color image data sets. The proposed method is compared to normal AdaBoost, classic relevance feedback and the state-of-the-art projection-based classifiers. The experiment results show the superior performance of i.Boost and the interactive boosting framework. Index Terms—Image classification, Information retrieval, Pattern recognition,Artificial intelligence, Algorithms
Q.: Adaptive discriminant projection for content-based image retrieval
- Proc. of Intl. Conf. on Pattern Recognition, Hong Kong
, 2006
"... Content-based Image Retrieval (CBIR) is a computer vision application that aims at automatically retrieving images based on their visual content. Linear Discriminat Analysis and its variants have been widely used in CBIR applications because of their effectiveness in finding a projection that maps t ..."
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Cited by 3 (1 self)
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Content-based Image Retrieval (CBIR) is a computer vision application that aims at automatically retrieving images based on their visual content. Linear Discriminat Analysis and its variants have been widely used in CBIR applications because of their effectiveness in finding a projection that maps the original high-dimensional space to a low-dimensional one and preserves the most discriminant features. Those techniques assume images from certain class(es) are all visually similar and try to cluster them in the projected space. In this paper we show that the human high-level concept of semantic similarity between images may not arise only from the low-level visual similarity and consequently that assumption is inappropriate in many cases. We propose an Adaptive Discrimant Projection framework which could model different data distributions based on the clustering of different classes. To learn the best model fitting the real scenario, Boosted Adaptive Discriminant Projection is further proposed. Extensive experiments are designed to evaluate our methods and compare them to the state-of-the-art techniques on benchmark data set and real image retrieval applications. The results show the superior performance of our proposed methods.
Constructing descriptive and discriminant features for face classification
- in IEEE International Conference on Acoustics, Speech and Signal Processing
, 2006
"... Linear Discriminant Analysis (LDA) has been widely applied in the field of face classification because of its simplicity and efficiency in capturing the most discriminant features. However LDA often fails when facing the small sample set and change in illumination, pose or expression. To overcome th ..."
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Cited by 2 (0 self)
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Linear Discriminant Analysis (LDA) has been widely applied in the field of face classification because of its simplicity and efficiency in capturing the most discriminant features. However LDA often fails when facing the small sample set and change in illumination, pose or expression. To overcome those difficulties, Principal Component Analysis (PCA), which recovers the most descriptive/informative features in the dimension-reduced feature space, is often used in the preprocessing stage. Although there is a trend of preferring LDA to PCA in classification, it has been found that PCA may perform better than LDA in some cases, especially when the size of the training set is small. In this paper we propose a parametric framework that can unify PCA and LDA to find both discriminant and descriptive features. To avoid the exhaustive parameter searching, we incorporate a non-linear boosting process to enhance a pool of hybrid classifiers and adaptively combine them into a more accurate one. To evaluate the performance of our boosted hybrid method, we compare it to state-of-the-art LDA variants and the other PCA-LDA techniques on three widely used face image benchmark databases. The experiment results show the superior performance of our novel boosted hybrid discriminant analysis.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2005 1 Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms.
, 2005
"... We present a novel approach for retrieval of object categories based on a novel type of image representation: the Generalized Correlogram (GC). In our image representation, the object is described as a constellation of GCs where each one encodes information about some local part and the spatial rela ..."
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We present a novel approach for retrieval of object categories based on a novel type of image representation: the Generalized Correlogram (GC). In our image representation, the object is described as a constellation of GCs where each one encodes information about some local part and the spatial relations from this part to others (i.e. the part’s context). We show how such a representation can be used with fast procedures that learn the object category with weak supervision and efficiently match the model of the object against large collections of images. In the learning stage, we show that by integrating our representation with Boosting the system is able to obtain a compact model that is represented by very few features, where each feature conveys key properties about the object’s parts and their spatial arrangement. In the matching step, we propose direct procedures that exploit our representation for efficiently considering spatial coherence between the matching of local parts. Combined with an appropriate data organization such as Inverted Files, we show that thousands of images can be evaluated efficiently. The framework has been applied to the standard CALTECH database with seven object categories and clutter, and we show that our results are favorably compared against state-of-the-art methods in both computational cost and accuracy. Index Terms object recognition, retrieval, boosting, spatial pattern, contextual information
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"... Abstract —In this paper, a novel hybrid dimension reduction technique for classification is proposed based on the hybrid analysis of principal component analysis (PCA) and linear discriminant analysis (LDA). LDA is known for capturing the most discriminant features of the data in the projected space ..."
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Abstract —In this paper, a novel hybrid dimension reduction technique for classification is proposed based on the hybrid analysis of principal component analysis (PCA) and linear discriminant analysis (LDA). LDA is known for capturing the most discriminant features of the data in the projected space while PCA is known for preserving the most descriptive ones after projection. Our hybrid technique integrates discriminant and descriptive information and finds a richer set of alternatives beyond LDA and PCA in a 2D parametric space, which fits a specific classification task and data distribution better. Theoretical study shows that our technique also alleviates the singularity problem of scatter matrix, which is caused by small training set, and increases the effective dimension of the projected subspace. In order to find the hybrid features adaptively and avoid exhaustive parameter searching, we further propose a boosted hybrid analysis method that incorporates a non-linear boosting process to enhance a set of hybrid classifiers and combine them into a more accurate one. Compared with the other techniques that aim at combining PCA and LDA, our approaches are novel because our method finds alternatives to LDA and PCA in a 2D parameter space and the boosting process provides enhancement and robust combination of the classifiers. Extensive experiments are conducted on benchmark and real image databases to compare our proposed methods to the state-of-the-art linear and non-linear discriminant analysis techniques. The results show the superior performance of our hybrid analysis methods. Index Terms—Image classification, Information retrieval,
