Wavelet-Based Salient Points: Applications to Image Retrieval Using Color and Texture Features

Loupias, Etienne; Sebe, Nicu

doi:10.1007/3-540-40053-2_20

Cited by 20 publications

(12 citation statements)

References 9 publications

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“…In these experiments we also considered random points in our comparison [12]. For each image in the database we computed the mean number m of salient points extracted by different detectors and then we selected m random points using a uniform distribution.…”

Section: Results For Information Contentmentioning

confidence: 99%

Evaluation of salient point techniques

Sebe

Tian

Loupias

et al. 2003

Image and Vision Computing

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Section: Results For Information Contentmentioning

confidence: 99%

Evaluation of salient point techniques

Sebe

Tian

Loupias

et al. 2003

Image and Vision Computing

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“…al. [5] proposed wavelet-based salient point detectors. The wavelet transform has several appealing properties such as the multi-scale representation and the spatial-frequency localization.…”

confidence: 99%

Appearance-based Salient Features Extraction in Medical Images Using Sparse Contourlet-based Representation

Zewail¹,

Hag-ElSafi²

2017

IJIGSP

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Abstract-Medical experts often examine hundreds of xray images searching for salient features that are used to detect pathological abnormalities.Inspired by our understanding of the human visual system, automated salient features detection methods have drawn much attention in the medical imaging research community. However, despite the efforts, detecting robust and stable salient features in medical images continues to constitute a challenging task. This is mainly attributed to the complexity of the anatomical structures of interest which usually undergo a wide range of rigid and non-rigid variations.In this paper, we present a novel appearance-based salient feature extraction and matching method based on sparse Contourlet-based representation. The multi-scale and directional capabilities of the Contourlets is utilized to extract salient points that are robust to noise, rigid and non-rigid deformations. Moreover, we also include prior knowledge about local appearance of the salient points of the structure of interest. This allows for extraction of robust stable salient points that are most relevant to the anatomical structure of interest.

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“…pixel locations at which the image signal changes twodimensionally (see e.g. Schmid and Mohr 1997, Bres and Jolion 1999, Loupias and Sebe 2000, Amsaleg and Gros 2001.…”

Section: Visual Featuresmentioning

confidence: 99%

Content-Based Image Retrieval Using Self-Organizing Maps

Laaksonen

Koskela

Oja

1999

Visual Information and Information Systems

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Digital image libraries are becoming more common and widely used as visual information is produced at a rapidly growing rate. Creating and storing digital images is nowadays easy and getting more affordable all the time as the needed technologies are maturing and becoming eligible for general use. As a result, the amount of data in visual form is increasing and there is a strong need for effective ways to manage and process it. In many settings, the existing and widely adopted methods for text-based indexing and information retrieval are inadequate for these new purposes.Content-based image retrieval addresses the problem of finding images relevant to the users' information needs from image databases, based principally on low-level visual features for which automatic extraction methods are available. Due to the inherently weak connection between the high-level semantic concepts that humans naturally associate with images and the low-level visual features that the computer is relying upon, the task of developing this kind of systems is very challenging. A popular method to improve retrieval performance is to shift from single-round queries to navigational queries where a single retrieval instance consists of multiple rounds of user-system interaction and query reformulation. This kind of operation is commonly referred to as relevance feedback and can be considered as supervised learning to adjust the subsequent retrieval process by using information gathered from the user's feedback.In this thesis, an image retrieval system named PicSOM is presented, including detailed descriptions of using multiple parallel Self-Organizing Maps (SOMs) for image indexing and a novel relevance feedback technique. The proposed relevance feedback technique is based on spreading the user responses to local SOM neighborhoods by a convolution with a kernel function. A broad set of evaluations with different image features, retrieval tasks, and parameter settings demonstrating the validity of the retrieval method is described. In particular, the results establish that relevance feedback with the proposed method is able to adapt to different retrieval tasks and scenarios.Furthermore, a method for using the relevance assessments of previous retrieval sessions or potentially available keyword annotations as sources of semantic information is presented. With performed experiments, it is confirmed that the efficiency of semantic image retrieval can be substantially increased by using these features in parallel with the standard low-level visual features.

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Wavelet-Based Salient Points: Applications to Image Retrieval Using Color and Texture Features

Cited by 20 publications

References 9 publications

Evaluation of salient point techniques

Evaluation of salient point techniques

Appearance-based Salient Features Extraction in Medical Images Using Sparse Contourlet-based Representation

Content-Based Image Retrieval Using Self-Organizing Maps

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