Applying logistic regression to relevance feedback in image retrieval systems

León, Teresa; Zuccarello, Pedro; Ayala, Guillermo; Ves, Esther de; Domingo, Juan Carlos Ureta

doi:10.1016/j.patcog.2007.02.002

Cited by 57 publications

(51 citation statements)

References 15 publications

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“…These include decision tree [76], relevance vector machines [36], logistics regression [71] and boosting [75]. However, these methods have been used more for the traditional classification problems than for the fusion problems.…”

Section: Remarks On Classification-based Fusion Methodsmentioning

confidence: 99%

Multimodal fusion for multimedia analysis: a survey

et al. 2010

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This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.

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Section: Remarks On Classification-based Fusion Methodsmentioning

confidence: 99%

Multimodal fusion for multimedia analysis: a survey

et al. 2010

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“…Classifiers based on Bayesian network (Cox et al, 2000;De Ves et al, 2006), Self organizing maps (Koskela et al, 2004), Support vector machine (Zhou and Huang, 2001;Tong and Chang, 2001) and Regression models (Leon et al, 2007) have been proposed with varying classification accuracy. Ghrare et al (2009) proposed lossless coding for the image retrieval problem by using lossless compression with high accuracy.…”

Section: Methodsmentioning

confidence: 99%

Content Based Image Retrieval using Novel Gaussian Fuzzy Feed Forward-Neural Network

Durai¹

2011

Journal of Computer Science

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Problem statement: With extensive digitization of images, diagrams and paintings, traditional keyword based search has been found to be inefficient for retrieval of the required data. Content-Based Image Retrieval (CBIR) system responds to image queries as input and relies on image content, using techniques from computer vision and image processing to interpret and understand it, while using techniques from information retrieval and databases to rapidly locate and retrieve images suiting an input query. CBIR finds extensive applications in the field of medicine as it assists a doctor to make better decisions by referring the CBIR system and gain confidence. Approach: Various methods have been proposed for CBIR using image low level image features like histogram, color layout, texture and analysis of the image in the frequency domain. Similarly various classification algorithms like Naïve Bayes classifier, Support Vector Machine, Decision tree induction algorithms and Neural Network based classifiers have been studied extensively. We proposed to extract features from an image using Discrete Cosine Transform, extract relevant features using information gain and Gaussian Fuzzy Feed Forward Neural Network algorithm for classification. Results and Conclusion: We apply our proposed procedure to 180 brain MRI images of which 72 images were used for testing and the remaining for training. The classification accuracy obtained was 95.83% for a three class problem. This research focused on a narrow search, where further investigation is needed to evaluate larger classes.

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“…The two distance metrics used are: the Euclidean and the Mahalanobis. The so-called OWA [4] operators have been used to aggregate the three low-level feature vectors of the topic images. ENRICH.…”

Section: Detailed Description Of Experimentsmentioning

confidence: 99%

“…This paper describes our participation at the ImageCLEF Photographic Retrieval task of CLEF 2008, fully described in [1,2]. This campaign Mir-FI team (MIRACLE at UPM) joined the Vision-Team at the University of Valencia (UV) who has developed a Content-Based retrieval system (CBIR) [4], in which the low-level features have been adapted to be used at the ImageCLEFphoto.…”

Section: Introductionmentioning

confidence: 99%

Some Results Using Different Approaches to Merge Visual and Text-Based Features in CLEF’08 Photo Collection

Garcı́a-Serrano

Benavent

Granados

et al. 2009

Lecture Notes in Computer Science

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Abstract. This paper describes the participation of the MIRACLE team 1 at the ImageCLEF Photographic Retrieval task of CLEF 2008. We succeeded in submitting 41 runs. Obtained results from text-based retrieval are better than content-based as previous experiments in the MIRACLE team campaigns [5,6] using different software. Our main aim was to experiment with several merging approaches to fuse text-based retrieval and content-based retrieval results, and it happened that we improve the text-based baseline when applying one of the three merging algorithms, although visual results are lower than textual ones.

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Applying logistic regression to relevance feedback in image retrieval systems

Cited by 57 publications

References 15 publications

Multimodal fusion for multimedia analysis: a survey

Multimodal fusion for multimedia analysis: a survey

Content Based Image Retrieval using Novel Gaussian Fuzzy Feed Forward-Neural Network

Some Results Using Different Approaches to Merge Visual and Text-Based Features in CLEF’08 Photo Collection

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