Localized Content-Based Image Retrieval

Rahmani, Rouhollah; Goldman, Sally A.; Zhang, Hui; Cholleti, Sharath R.; Fritts, Jason E.

doi:10.1109/tpami.2008.112

Cited by 158 publications

(105 citation statements)

References 27 publications

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“…In most literature the size usually selected form the common windows sizes such as 3 × 3, 5 × 5, or 7 × 7 (Kim & Milanfar, 2013), (Banerjee & Kundu, 2003), and (Rahmani, et al, 2008).…”

Section: Saliency Evaluationmentioning

confidence: 99%

Irregularity-based image regions saliency identification and evaluation

Al-Azawi

Yang

Istance

2014

Multimed Tools Appl

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IntroductionSalient points, objects, or regions extraction is still a challenging topic of research and its definition is still very general. Different literature have defined the saliency in different ways. In general and based on human vision system HVS, the salient object is the object that may capture the attention or attract the HVS. HVS uses two stages to identify the objects, preattentive and attentive stages (Kadir & Brady, 2001).In pre-attentive stage, the local regions of image that present spatial discontinuity are detected. In the attentive stage, relationships between these regions are found, and grouped together. The rest of the paper will be organized as follows: previous work is presented in section 2. In Section 3, we will explain the proposed saliency extraction technique based on the irregularity of the regions in an image. In section 44, a new automatic thresholding technique will be proposed. Points clustering and region merging will be discussed in section 5. Section 6 will review the saliency evaluation technique and introduce a new method that is suitable to the application at hand. In section 7 we will discuss the experimental results and the optimum window size selection. Finally, conclusions will be derived in section 8. 2 Previous WorkWavelet was used by many authors such as in (Loupias, et al., 2000), (Tian, et al., 2001), (Song, et al., 2006), (Lin & Yang, 2007), and (Arivazhagan & Shebiah, 2009) In this paper, we shall introduce a robust and general saliency extraction method that is fully automated and does not need any intervention from the user. In addition, it does need any pre-knowledge about the image. Irregularity as a Measure of SaliencyIn this Section, we shall discuss the proposed technique. First, we shall define the saliency as any irregular region in the image with nature differs from the nature of the image as a whole; for example, a region with texture in a uniform region, or vice versa, thus we shall define irregularity as the main measure of saliency.Let ℐ( , ) be the image intensity at position ( , ), where = 1, 2, … . , , = 1,2, … , , with and are the image width and height respectively. The image is represented as a set of pixels and is defined as:Where p xy is the pixel's value at location ( , ) and ℕ is the set of natural numbers. Furthermore, we shall define the set ℙ = { | ⊆ } as the power set of .According to the application at hands, the main constrain is that; all the elements of a given subset should belong to a connected region which means they should be in a given neighbouring area.The sub-image, which we shall refer to as a region, is a subset of the image with elements that belong to the same region. The size of this sub-image is × ℎ.Considering that the regions are disjoint, they can be defined as:Assuming that there is no overlapping between the adjacent regions, the total number of regions is equal to × ℎ . In most cases there should be overlapping between the moving windows, if we denote the overlapping value by , then the total numbers of wind...

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“…In most literature the size usually selected form the common windows sizes such as 3 × 3, 5 × 5, or 7 × 7 (Kim & Milanfar, 2013), (Banerjee & Kundu, 2003), and (Rahmani, et al, 2008).…”

Section: Saliency Evaluationmentioning

confidence: 99%

Irregularity-based image regions saliency identification and evaluation

Al-Azawi

Yang

Istance

2014

Multimed Tools Appl

View full text Add to dashboard Cite

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“…Several researches were conducted to study the multi-instance learning measure to select the semantic regions from query images automatically [8] [9]. The selected regions from query images are labeled as common positive from users' relevance feedback.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Content Based Image Retrieval in P2p Systems

Swathi¹

2014

IJRET

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“…An effective approach to classification of a landmark image is to harvest a large number of similarly annotated landmark images, and then to match it based on context and content features of these images [14] [15]. In this process image and object matching using interest point features has been shown to work well even in large-scale image databases containing thousands of different images [11].…”

Section: Landmark Classificationmentioning

confidence: 99%

Automated Annotation of Landmark Images Using Community Contributed Datasets and Web Resources

Jones

Byrne

Hughes

et al. 2011

Semantic Multimedia

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Abstract.A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching offers 93.6% classification accuracy; the selection of appropriate tags for use in annotation performs well (F1M of 0.59), and it subsequently automatically identifies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases.

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Localized Content-Based Image Retrieval

Cited by 158 publications

References 27 publications

Irregularity-based image regions saliency identification and evaluation

Irregularity-based image regions saliency identification and evaluation

Optimizing Content Based Image Retrieval in P2p Systems

Automated Annotation of Landmark Images Using Community Contributed Datasets and Web Resources

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