The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Ebroul Izquierdo - One of the best experts on this subject based on the ideXlab platform.
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Query refinement and User Relevance Feedback for contextualized image retrieval
5th International Conference on Visual Information Engineering (VIE 2008), 2008Co-Authors: Krishna Chandramouli, Tomáš Kliegr, Jan Nemrava, Vojtech Svátek, Ebroul IzquierdoAbstract:The motivation of this paper is to enhance the User perceived precision of results of content based information retrieval (CBIR) systems with query refinement (QR), visual analysis (VA) and Relevance Feedback (RF) algorithms. The proposed algorithms were implemented as modules into K-Space CBIR system. The QR module discovers hypernyms for the given query from a free text corpus (such as Wikipedia) and uses these hypernyms as refinements for the original query. Extracting hypernyms from Wikipedia makes it possible to apply query refinement to more queries than in related approaches that use static predefined thesaurus such as Wordnet. The VA Module uses the K-Means algorithm for clustering the images based on low-level MPEG - 7 Visual features. The RF Module uses the preference information expressed by the User to build User profiles by applying SOM- based supervised classification, which is further optimized by a hybrid Particle Swarm Optimization (PSO) algorithm. The experiments evaluating the performance of QR and VA modules show promising results.
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an object and User driven system for semantic based image annotation and retrieval
IEEE Transactions on Circuits and Systems for Video Technology, 2007Co-Authors: D Djordjevic, Ebroul IzquierdoAbstract:In this paper, a system for object-based semi-automatic indexing and retrieval of natural images is introduced. Three important concepts underpin the proposed system: a new strategy to fuse different low-level content descriptions; a learning technique involving User Relevance Feedback; and a novel object based model to link semantic terms and visual objects. To achieve high accuracy in the retrieval and subsequent annotation processes several low-level image primitives are combined in a suitable multifeatures space. This space is modelled in a structured way exploiting both low-level features and spatial contextual relations of image blocks. Support vector machines are used to learn from gathered information through Relevance Feedback. An adaptive convolution kernel is defined to handle the proposed structured multifeature space. The positive definite property of the introduced kernel is proven, as essential condition for uniqueness and optimality of the convex optimization in support vector machines. The proposed system has been thoroughly evaluated and selected results are reported in this paper
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ICIP (6) - A Knowledge Structuring Technique for Image Classification
2007 IEEE International Conference on Image Processing, 2007Co-Authors: Le Dong, Ebroul IzquierdoAbstract:A system for image analysis and classification based on a knowledge structuring technique is presented. The knowledge structuring technique automatically creates a Relevance map from salient areas of natural images. It also derives a set of well-structured representations from low-level description to drive the final classification. The backbone of the knowledge structuring technique is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology representation module based on a growing cell structure network. Classification is achieved by simulating high-level top-down visual information perception and classifying using an incremental Bayesian parameter estimation method. The proposed modular system architecture offers straightforward expansion to include User Relevance Feedback, contextual input, and multimodal information if available.
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using Relevance Feedback to bridge the semantic gap
Lecture Notes in Computer Science, 2006Co-Authors: Ebroul Izquierdo, D DjordjevicAbstract:In this article relevant developments in Relevance Feedback based image annotation and retrieval are reported. A new approach to infer semantic concepts representing meaningful objects in images is also described. The proposed technique combines User Relevance Feedback and underlying low-level properties of elementary building blocks making up semantic objects in images. Images are regarded as mosaics made of small building blocks featuring good representations of colour, texture and edgeness. The approach is based on accurate classification of these building blocks. Once this has been achieved, a signature for the object of concern is built. It is expected that this signature features a high discrimination power and consequently it becomes very suitable to find other images containing the same semantic object. The model combines fuzzy clustering and Relevance Feedback in the training stage, and uses fuzzy support vector machines in the generalization stage.
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VISUAL - A knowledge synthesizing approach for classification of visual information
Lecture Notes in Computer Science, 1Co-Authors: Le Dong, Ebroul IzquierdoAbstract:An approach for visual information analysis and classification is presented. It is based on a knowledge synthesizing technique to automatically create a Relevance map from essential areas in natural images. It also derives a set of well-structured representations from low-level description to drive the final classification. The backbone of this approach is a distribution mapping strategy involving a knowledge synthesizing module based on an intelligent growing when required network. Classification is achieved by simulating the high-level top-down visual information perception in primates followed by incremental Bayesian parameter estimation. The proposed modular system architecture offers straightforward expansion to include User Relevance Feedback, contextual input, and multimodal information if available.
Minkoo Kim - One of the best experts on this subject based on the ideXlab platform.
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musemble a novel music retrieval system with automatic voice query transcription and reformulation
Journal of Systems and Software, 2008Co-Authors: Seungmin Rho, Byeong-jun Han, Eenjun Hwang, Minkoo KimAbstract:So far, many researches have been done to develop efficient music retrieval systems, and query-by-humming has been considered as one of the most intuitive and effective query methods for music retrieval. For the voice humming to be a reliable query source, elaborate signal processing and acoustic similarity measurement schemes are necessary. On the other hand, recently, there has been an increased interest in query reformulation using Relevance Feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have not been exploited widely in the field of music retrieval. In this paper, we develop a novel music retrieval system called MUSEMBLE (MUSic enEMBLE) based on two distinct features: (i) A sung or hummed query is automatically transcribed into a sequence of pitch and duration pairs with improved accuracy for music representation. More specifically, we developed two new and unique techniques called WAE (windowed average energy) and dynamic ADF (amplitude-based difference function) onsets for more accurate note segmentation and onset/offset detection in acoustic signal, respectively. The former improved energy-based approaches such as AE by defining small but coherent windows with local and global threshold values. On the other hand, the latter improved the AF (amplitude function) that calculates the summation of the absolute values of signal differences for the clustering energy contour. (ii) A User query is reformulated using User Relevance Feedback with a genetic algorithm to improve retrieval performance. Even though we have especially focused on humming queries in this paper, MUSEMBLE provides versatile query and browsing interfaces for various kinds of Users. We have carried out extensive experiments on the prototype system to evaluate the performance of our voice query transcription and genetic algorithm-based Relevance Feedback schemes. We demonstrate that our proposed method improves the retrieval accuracy up to 20-40% compared with other popular RF methods. We also show that both WAE and Dynamic ADF methods improve the transcription accuracy up to 95%.
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ICME - MUSEMBLE: A Music Retrieval System Based on Learning Environment
Multimedia and Expo 2007 IEEE International Conference on, 2007Co-Authors: Seungmin Rho, Byeong-jun Han, Eenjun Hwang, Minkoo KimAbstract:Query reformulation has been suggested as an effective way to improve retrieval efficiency in text information retrieval and one of the well-known techniques for query reformulation is User Relevance Feedback. Recently, there has been an increased interest in the query reformulation using Relevance Feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have still not been exploited widely in the field of music retrieval. In this paper, we propose a novel music retrieval scheme that is based on User Relevance Feedback with genetic algorithm and evolutionary method with neural network. The former is for reformulating a User query and the latter is for reducing the population size by learning neural network. We implemented a prototype music retrieval system called MUSEMBLE based on this scheme. Experimental results showed that our proposed scheme achieves a good performance.
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WIAMIS - Voice Query Transcription and Expansion Scheme for Efficient Music Retrieval
Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07), 2007Co-Authors: Seungmin Rho, Eenjun Hwang, Han Byeong-jun, Minkoo KimAbstract:In this paper, we present a scheme for efficient humming-based music retrieval. For that purpose, we first describe how to extract a sequence of pitch and duration pairs as its feature information from sung or hummed query accurately and automatically. And then, we propose a novel scheme for reformulating User query to improve retrieval performance. The scheme is based on User Relevance Feedback with genetic algorithm. We implemented a prototype system based on these scheme and performed various experiments. Experimental result shows that our proposed scheme achieves an impressive performance.
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MUE - Music Information Retrieval Using a GA-based Relevance Feedback
2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07), 2007Co-Authors: Seungmin Rho, Eenjun Hwang, Minkoo KimAbstract:Recently, there has been an increased interest in the query reformulation using Relevance Feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have still not been exploited widely in the field of music retrieval. In this paper, we propose a novel music retrieval scheme that incorporates User Relevance Feedback with genetic algorithm to improve retrieval performance and develop a prototype system based on it. Our system also provides interesting easy-to-use graphical User interfaces. For example, Users can browse and play query results easily using markers in the music indicating those matched parts for the query. By performing various experiments, we show the effectiveness and efficiency of our proposed scheme.
Shyiming Chen - One of the best experts on this subject based on the ideXlab platform.
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A new query expansion method for document retrieval based on the inference of fuzzy rules
Journal of the Chinese Institute of Engineers, 2007Co-Authors: Yu-chuan Chang, Shyiming Chen, Churn-jung LiauAbstract:Abstract Automatic query expansion based on User Relevance Feedback techniques can improve the performance of document retrieval systems. In this paper, we present a new query expansion method based on the inference of fuzzy rules and User Relevance Feedback techniques to deal with document retrieval. The proposed method uses membership functions and fuzzy rules to infer relevant degrees of expansion terms and puts the expansion terms with larger relevant degrees into the original User's query. Then, the system calculates the degree of similarity of each document with respect to the expanded User's query. The proposed method gets a higher average precision rate and a higher average recall rate than the existing methods for document retrieval.
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Query expansion for document retrieval based on fuzzy rules and User Relevance Feedback techniques
Expert Systems with Applications, 2006Co-Authors: Hsiching Lin, Lihui Wang, Shyiming ChenAbstract:Abstract In document retrieval systems, proper query terms significantly affect the performance of document retrieval systems. The performance of the systems can be improved by using query expansion techniques. In this paper, we present a new method for query expansion based on User Relevance Feedback techniques for mining additional query terms. According to the User's Relevance Feedback, the proposed query expansion method calculates the degrees of importance of relevant terms of documents in the document database. The relevant terms have higher degrees of importance may become additional query terms. The proposed method uses fuzzy rules to infer the weights of the additional query terms. Then, the weights of the additional query terms and the weights of the original query terms are used to form the new query vector, and we use this new query vector to retrieve documents. The proposed query expansion method increases the precision rates and the recall rates of information retrieval systems for dealing with document retrieval. It gets a higher average recall rate and a higher average precision rate than the method presented in Chang, Y. C., Chen, S. M., & Liau, C. J. (2003). A new query expansion method based on fuzzy rules. Proceedings of the Seventh Joint Conference on AI, Fuzzy System, and Grey System , Taipei, Taiwan, Republic of China.
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a new method for query expansion based on User Relevance Feedback techniques
한국지능시스템학회 국제학술대회 발표논문집, 2005Co-Authors: Lihui Wang, Hsiching Lin, Shyiming ChenAbstract:In this paper, we present a new method for query expansion based on User Relevance Feedback techniques for mining additional query terms. According to the User's Relevance Feedback, the proposed query expansion method calculates the degrees of importance of relevant terms of documents in the document database. The relevant terms have higher degrees of importance may become additional query terms. The proposed method uses fuzzy rules to infer the weights of the additional query terms. Then, the weights of the additional query terms and the weights of the original query terms to form the new query vector, and we use this new query vector to retrieve documents. The proposed query expansion method increases the precision rates and the recall rates of information retrieval systems for dealing with document retrieval. It gets a higher average recall rate and a higher average precision rate than the ones by using the method presented in [4].
Seungmin Rho - One of the best experts on this subject based on the ideXlab platform.
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musemble a novel music retrieval system with automatic voice query transcription and reformulation
Journal of Systems and Software, 2008Co-Authors: Seungmin Rho, Byeong-jun Han, Eenjun Hwang, Minkoo KimAbstract:So far, many researches have been done to develop efficient music retrieval systems, and query-by-humming has been considered as one of the most intuitive and effective query methods for music retrieval. For the voice humming to be a reliable query source, elaborate signal processing and acoustic similarity measurement schemes are necessary. On the other hand, recently, there has been an increased interest in query reformulation using Relevance Feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have not been exploited widely in the field of music retrieval. In this paper, we develop a novel music retrieval system called MUSEMBLE (MUSic enEMBLE) based on two distinct features: (i) A sung or hummed query is automatically transcribed into a sequence of pitch and duration pairs with improved accuracy for music representation. More specifically, we developed two new and unique techniques called WAE (windowed average energy) and dynamic ADF (amplitude-based difference function) onsets for more accurate note segmentation and onset/offset detection in acoustic signal, respectively. The former improved energy-based approaches such as AE by defining small but coherent windows with local and global threshold values. On the other hand, the latter improved the AF (amplitude function) that calculates the summation of the absolute values of signal differences for the clustering energy contour. (ii) A User query is reformulated using User Relevance Feedback with a genetic algorithm to improve retrieval performance. Even though we have especially focused on humming queries in this paper, MUSEMBLE provides versatile query and browsing interfaces for various kinds of Users. We have carried out extensive experiments on the prototype system to evaluate the performance of our voice query transcription and genetic algorithm-based Relevance Feedback schemes. We demonstrate that our proposed method improves the retrieval accuracy up to 20-40% compared with other popular RF methods. We also show that both WAE and Dynamic ADF methods improve the transcription accuracy up to 95%.
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ICME - MUSEMBLE: A Music Retrieval System Based on Learning Environment
Multimedia and Expo 2007 IEEE International Conference on, 2007Co-Authors: Seungmin Rho, Byeong-jun Han, Eenjun Hwang, Minkoo KimAbstract:Query reformulation has been suggested as an effective way to improve retrieval efficiency in text information retrieval and one of the well-known techniques for query reformulation is User Relevance Feedback. Recently, there has been an increased interest in the query reformulation using Relevance Feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have still not been exploited widely in the field of music retrieval. In this paper, we propose a novel music retrieval scheme that is based on User Relevance Feedback with genetic algorithm and evolutionary method with neural network. The former is for reformulating a User query and the latter is for reducing the population size by learning neural network. We implemented a prototype music retrieval system called MUSEMBLE based on this scheme. Experimental results showed that our proposed scheme achieves a good performance.
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WIAMIS - Voice Query Transcription and Expansion Scheme for Efficient Music Retrieval
Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07), 2007Co-Authors: Seungmin Rho, Eenjun Hwang, Han Byeong-jun, Minkoo KimAbstract:In this paper, we present a scheme for efficient humming-based music retrieval. For that purpose, we first describe how to extract a sequence of pitch and duration pairs as its feature information from sung or hummed query accurately and automatically. And then, we propose a novel scheme for reformulating User query to improve retrieval performance. The scheme is based on User Relevance Feedback with genetic algorithm. We implemented a prototype system based on these scheme and performed various experiments. Experimental result shows that our proposed scheme achieves an impressive performance.
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MUE - Music Information Retrieval Using a GA-based Relevance Feedback
2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07), 2007Co-Authors: Seungmin Rho, Eenjun Hwang, Minkoo KimAbstract:Recently, there has been an increased interest in the query reformulation using Relevance Feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have still not been exploited widely in the field of music retrieval. In this paper, we propose a novel music retrieval scheme that incorporates User Relevance Feedback with genetic algorithm to improve retrieval performance and develop a prototype system based on it. Our system also provides interesting easy-to-use graphical User interfaces. For example, Users can browse and play query results easily using markers in the music indicating those matched parts for the query. By performing various experiments, we show the effectiveness and efficiency of our proposed scheme.
D Djordjevic - One of the best experts on this subject based on the ideXlab platform.
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an object and User driven system for semantic based image annotation and retrieval
IEEE Transactions on Circuits and Systems for Video Technology, 2007Co-Authors: D Djordjevic, Ebroul IzquierdoAbstract:In this paper, a system for object-based semi-automatic indexing and retrieval of natural images is introduced. Three important concepts underpin the proposed system: a new strategy to fuse different low-level content descriptions; a learning technique involving User Relevance Feedback; and a novel object based model to link semantic terms and visual objects. To achieve high accuracy in the retrieval and subsequent annotation processes several low-level image primitives are combined in a suitable multifeatures space. This space is modelled in a structured way exploiting both low-level features and spatial contextual relations of image blocks. Support vector machines are used to learn from gathered information through Relevance Feedback. An adaptive convolution kernel is defined to handle the proposed structured multifeature space. The positive definite property of the introduced kernel is proven, as essential condition for uniqueness and optimality of the convex optimization in support vector machines. The proposed system has been thoroughly evaluated and selected results are reported in this paper
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using Relevance Feedback to bridge the semantic gap
Lecture Notes in Computer Science, 2006Co-Authors: Ebroul Izquierdo, D DjordjevicAbstract:In this article relevant developments in Relevance Feedback based image annotation and retrieval are reported. A new approach to infer semantic concepts representing meaningful objects in images is also described. The proposed technique combines User Relevance Feedback and underlying low-level properties of elementary building blocks making up semantic objects in images. Images are regarded as mosaics made of small building blocks featuring good representations of colour, texture and edgeness. The approach is based on accurate classification of these building blocks. Once this has been achieved, a signature for the object of concern is built. It is expected that this signature features a high discrimination power and consequently it becomes very suitable to find other images containing the same semantic object. The model combines fuzzy clustering and Relevance Feedback in the training stage, and uses fuzzy support vector machines in the generalization stage.
