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

Sayad, Ismail El; Martinet, Jean; Urruty, Thierry; Amir, Samir; Djeraba, Chabane

doi:10.1145/1971519.1971556

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…21 In 22 PLSA is proposed to remove noisy visual words. This approach is further extended with the concept of meaningfulness in, 23 obtaining reductions of up to 92% of the vocabulary size without significant effect on image classification accuracy.…”

Section: Meaningfulness Of Visual Wordsmentioning

confidence: 99%

Texture classification of anatomical structures in CT using a context-free machine learning approach

Toro

Foncubierta–Rodríguez

Depeursinge

et al. 2015

SPIE Proceedings

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Medical images contain a large amount of visual information about anatomical structures and radiological signs of the patients. To make sense of this information, human interpretation is often essential. On the other hand, computer-based approaches can exploit information contained in the images by numerically measuring and quantifying specific visual features. Annotation of organs and other anatomical regions is an important step before computing more in depth numerical features on medical images. A texture-based organ classification algorithm that could be used to reduce the time required for annotating medical images is presented in this paper. The texture of organs is analyzed using a combination of state-of-the-art techniques: the Riesz transform and a bag of meaningful visual words. The effect of a meaningfulness transformation in the visual word space yields two important advantages that can be seen in the results. The number of descriptors is enormously reduced down to 10% of the original size, whereas classification accuracy is improved by up to 25% with respect to the baseline approach.

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Section: Meaningfulness Of Visual Wordsmentioning

confidence: 99%

Texture classification of anatomical structures in CT using a context-free machine learning approach

Toro

Foncubierta–Rodríguez

Depeursinge

et al. 2015

SPIE Proceedings

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“…Since it does not set any requirements on the nature of the low level features that yield these co-occurence matrices (other than being discrete), the extension to visual words is simple. PLSA in combination with visual words for classification purposes was also applied in [28,29].…”

Section: Plsa For Visual Wordsmentioning

confidence: 99%

Medical image retrieval using bag of meaningful visual words

Foncubierta–Rodríguez

Herrera

Müller

2013

Proceedings of the 1st ACM International Workshop on Multimedia Indexing and Information Retrieval for Healthcare

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Content-based medical image retrieval has been proposed as a technique that allows not only for easy access to images from the relevant literature and electronic health records but also for training physicians, for research and clinical decision support. The bag-of-visual-words approach is a widely used technique that tries to shorten the semantic gap by learning meaningful features from the dataset and describing documents and images in terms of the histogram of these features. Visual vocabularies are often redundant, over-complete and noisy. Larger than required vocabularies lead to high-dimensional feature spaces, which present important disadvantages with the curse of dimensionality and computational cost being the most obvious ones. In this work a visual vocabulary pruning technique is presented. It enormously reduces the amount of required words to describe a medical image dataset with no significant effect on the accuracy. Results show that a reduction of up to 90% can be achieved without impact on the system performance. Obtaining a more compact representation of a document enables multimodal description as well as using classifiers requiring low-dimensional representations.

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“…To address this problem, a recent common trend consists in using textual information retrieval techniques, i.e. the techniques designed for textual documents should be adapted for the images [3]. A simple and well-known model used for this prurpose is the Bag-of-Words (BoW) model.…”

Section: Introductionmentioning

confidence: 99%

Content-based image retrieval in the topic space using SOM and LDA

Ayech

Amiri

2015

2015 3rd International Conference on Control, Engineering &Amp; Information Technology (CEIT)

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Content-Based Image Retrieval (CBIR) becomes necessary when dealing with large collections of images. Recently, interesting retrieval methods are based on Bag-of-Words (BoW) model. It allows the projection of an image in a vector space called the word space. Nevertheless, this space can be further refined in order to reflect the semantic content of an image. Indeed, it can be transformed into a lower dimensional vector space called the topic space. In this paper, we propose a retrieval system based on an unsupervised learning: First, a Self-Organizing Map (SOM) is learnt to construct the word space from the feature space. Then, the word space is transformed into the topic space using the Latent Dirichlet Allocation (LDA) model. Once learnt, our model allows to infer a compact and semantic image representation. Experiments are performed using a set of "vehicule" images from Pascal VOC 2007 dataset. Evaluation shows that the proposed retrieval system leads to encouraging results.

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

Cited by 7 publications

References 22 publications

Texture classification of anatomical structures in CT using a context-free machine learning approach

Texture classification of anatomical structures in CT using a context-free machine learning approach

Medical image retrieval using bag of meaningful visual words

Content-based image retrieval in the topic space using SOM and LDA

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