Flower Image Retrieval Based on Saliency Map

Hu, Xuelong; Wu, Huining; Zhang, Yuhui; Sun, Lei

doi:10.1109/is3c.2014.86

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Let us consider a neighborhood denoted by( , ), here P represents the sampling points and Q represents the radius. These sampling points lie around the center pixel ( , ) and at coordinates are (8) Now the LBP label for pixel ( , ) can be calculated as follows (9) Where ( )=1 if ( ≥0) and ( )=0 if <0 [15].…”

Section: Our Approachmentioning

confidence: 99%

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Flower Image Retrieval Using LBP, Wavelet Moments, Gabor Wavelet Features and Multiple Distance Functions

Azme¹,

Happy²,

Bulbul³

2018

AIVP

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Flower image retrieval is a significant and challenging problem in content-based image retrieval. In this paper, we propose a content-based method for retrieving flower images of specified specie from a database of flower images of various species. Firstly, we use wavelet moment, Gabor wavelet and Local Binary Pattern (LBP) independently to characterize all flower images in the database. Secondly, we represent a query flower image with Gabor wavelet, wavelet moment, and LBP individually and search images in the database analogous to the query image. The retrieval is accomplished through calculating similarities between the query image and the database images by employing a set of distance functions. Experimental evaluation of the approach reveals that the Gabor wavelet achieves superiority over the wavelet moment and LBP considerably. It is also indicated that the retrieval outcome can be improved through concatenating the Gabor wavelet, wavelet moment, and LBP features rather than utilizing them individually. 1 Introduction At present world, with the availability of image capturing devices such as digital cameras, mobile cameras, webcams, video cameras, image scanners, the size of digital image collection is increasing at a great rate. A computer system for image searching, browsing and retrieving from a large database of digital images is called an image retrieval system. It is a tool, required by users from various domains, including remote sensing, medicine, crime prevention, architecture, publishing, fashion, etc. From this point of view, there have been developed many prevalent image retrieval systems. There are two frameworks of image retrieval such as text-based image retrieval and content-based Image Retrieval. The text-based approach started since the early 1960s. In text-based image retrieval system, for finding similar types of images it is needed to search by the accurate textual description, filenames, caption, keywords etc. Because the images are annotated in the database by textual description, filenames, caption, keywords etc. But text-based image retrieval is oldest image searching system. There are many several problems of text-based image retrieval methods. Firstly, annotation is never complete because it depends on the goal of the annotation, some mistakes like spelling error, spelling difference (US vs.UK), weird abbreviation (particularly medical) etc. The use of keywords is not only complex but also insufficient to represent the content by the size of image databases growing etc. The keywords are not unique for one kind of searching. The problems of image queries by text-based approaches cannot be described at all, but tap into the visual features of images .For example, a query for all the images in the database with "classroom" in it will good results if we annotate all the images containing classroom but for the same annotations, a specific search for images with a chair, table, computer or student in tit will fail. Another problem with text based approach is the lack of uniform

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Section: Our Approachmentioning

confidence: 99%

“…Several years later, HU et al [9] developed another content-based flower image retrieval system. They researched on flower image retrieval algorithm based on saliency map.…”

mentioning

confidence: 99%

Flower Image Retrieval Using LBP, Wavelet Moments, Gabor Wavelet Features and Multiple Distance Functions

Azme¹,

Happy²,

Bulbul³

2018

AIVP

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“…c) Saliency Mask (SM): The ij vector is built using the local CNN features of the whole image, but this time weighting their frequencies using a saliency map generated using a computational model of visual saliency. Using saliency maps for object detection and recognition has been previously proposed in [17,11,3].…”

Section: Encoding the Target Imagesmentioning

confidence: 99%

Where is my Phone?

Reyes

Mohedano

McGuinness

et al. 2016

Proceedings of the First Workshop on Lifelogging Tools and Applications

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This work presents a retrieval pipeline and evaluation scheme for the problem of finding the last appearance of personal objects in a large dataset of images captured from a wearable camera. Each personal object is modelled by a small set of images that define a query for a visual search engine.The retrieved results are reranked considering the temporal timestamps of the images to increase the relevance of the later detections. Finally, a temporal interleaving of the results is introduced for robustness against false detections. The Mean Reciprocal Rank is proposed as a metric to evaluate this problem. This application could help into developing personal assistants capable of helping users when they do not remember where they left their personal belongings.

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