Evaluation of Salient Point Techniques

Sebe, Nicu; Loupias, Etienne; Lew, Michael S.; Huang, Thomas S.

doi:10.1007/3-540-45479-9_39

Cited by 36 publications

(30 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many Surveys that tried to investigate the most stateof-the-art evaluation methods such as (Sebe , et al, 2002), (Toet, 2011), (Gide & Karam, 2012), (Borji, et al., October 7-13, 2012), (Judd, et al, January 13, 2012), (Zhao & Koch, 2011) and others are available but no general method can be used with all algorithms.…”

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

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...

show abstract

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

show abstract

“…Various approaches have been suggested such as colour moments and Gabor texture descriptors [17] as well as scale invariant interest points [10] and affine invariant interest point detector [11]. Scale Invariant Feature Transformation (SIFT) have been introduced by [9] and have been shown to be superior to other descriptors [12].…”

Section: Data Representationmentioning

confidence: 99%

A Correlation Approach for Automatic Image Annotation

Hardoon

Saunders

Szedmák

et al. 2006

Advanced Data Mining and Applications

View full text Add to dashboard Cite

Abstract. The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learning for the automatic annotation of query images. We represent the images using scale invariant transformation descriptors in order to account for similar objects appearing at slightly different scales and transformations. The resulting descriptors are utilised as visual terms for each image. We first aim to annotate query images by retrieving images that are similar to the query image. This approach uses the analogy that similar images would be annotated similarly as well. We then propose an image annotation method that learns a direct mapping from image descriptors to keywords. We compare the semantic based methods of Latent Semantic Indexing and Kernel Canonical Correlation Analysis (KCCA), as well as using a recently proposed vector label based learning method known as Maximum Margin Robot.

show abstract

“…In previous work, it has been shown that content-based retrieval based on salient interest points and regions performs much better than global image descriptors [1,2]. For our content-based image retrieval algorithm, we select salient regions using the method described by Lowe [12], where scale-space peaks are detected in a multi-scale difference-of-Gaussian pyramid.…”

Section: Salient Regionsmentioning

confidence: 99%

“…There are a large number of different types of feature descriptors that have been suggested for describing the local image content within a salient region; for example, colour moments and Gabor texture descriptors [2]. The choice of local descriptor is in many respects dependent on the actual application of the retrieval system; for example, some applications may require colour, others may not.…”

Section: Local Feature Descriptorsmentioning

confidence: 99%

See 1 more Smart Citation

On Image Retrieval Using Salient Regions with Vector-Spaces and Latent Semantics

Hare

Lewis

2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The vector-space retrieval model and Latent Semantic Indexing approaches to retrieval have been used heavily in the field of text information retrieval over the past years. The use of these approaches in image retrieval, however, has been somewhat limited. In this paper, we present methods for using these techniques in combination with an invariant image representation based on local descriptors of salient regions. The paper also presents an evaluation in which the two techniques are used to find images with similar semantic labels.

show abstract

Evaluation of Salient Point Techniques

Cited by 36 publications

References 9 publications

Irregularity-based image regions saliency identification and evaluation

Irregularity-based image regions saliency identification and evaluation

A Correlation Approach for Automatic Image Annotation

On Image Retrieval Using Salient Regions with Vector-Spaces and Latent Semantics

Contact Info

Product

Resources

About