Content-based image retrieval by dictionary of local feature descriptors

Najgebauer, Patryk; Nowak, Tomasz; Romanowski, Jakub; Gabryel, Marcin; Korytkowski, Marcin; Scherer, Rafał

doi:10.1109/ijcnn.2014.6889815

Cited by 4 publications

(2 citation statements)

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“…For better performance the proposed method uses a special, dictionary-based form of keypoint representation [6] [9]. Dictionary-based structure accelerates the comparison process by allowing to skip most of keypoint combinations.…”

Section: Methods Descriptionmentioning

confidence: 99%

Fast Dictionary Matching for Content-Based Image Retrieval

Najgebauer

Rygał

Nowak

et al. 2015

Artificial Intelligence and Soft Computing

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Abstract. This paper describes a method for searching for common sets of descriptors between collections of images. The presented method operates on local interest keypoints, which are generated using the SURF algorithm. The use of a dictionary of descriptors allowed achieving good performance of the content-based image retrieval. The method can be used to initially determine a set of similar pairs of keypoints between images. For this purpose, we use a certain level of tolerance between values of descriptors, as values of feature descriptors are almost never equal but similar between different images. After that, the method compares the structure of rotation and location of interest points in one image with the point structure in other images. Thus, we were able to find similar areas in images and determine the level of similarity between them, even when images contain different scenes.

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Section: Methods Descriptionmentioning

confidence: 99%

Fast Dictionary Matching for Content-Based Image Retrieval

Najgebauer

Rygał

Nowak

et al. 2015

Artificial Intelligence and Soft Computing

Self Cite

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“…Describing image by set of features is a common approach in computer vision [15], [18], [19], [13], [9]. The feature vector is constructed to obtain the relevant information from the image data.…”

Section: Proposed Feature Descriptormentioning

confidence: 99%

Image Segmentation in Liquid Argon Time Projection Chamber Detector

Płoński

Stefan

Sulej

et al. 2015

Artificial Intelligence and Soft Computing

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Abstract. The Liquid Argon Time Projection Chamber (LAr-TPC) detectors provide excellent imaging and particle identification ability for studying neutrinos. An efficient and automatic reconstruction procedures are required to exploit potential of this imaging technology. Herein, a novel method for segmentation of images from LAr-TPC detectors is presented. The proposed approach computes a feature descriptor for each pixel in the image, which characterizes amplitude distribution in pixel and its neighbourhood. The supervised classifier is employed to distinguish between pixels representing particle's track and noise. The classifier is trained and evaluated on the hand-labeled dataset. The proposed approach can be a preprocessing step for reconstructing algorithms working directly on detector images.

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