Irregularity-based image regions saliency identification and evaluation

Abstract

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...

Saliency-Based Image Retrieval as a Refinement to Content-Based Image Retrieval

“…The strengths and weaknesses of the mentioned methods are beyond the scope of this paper, a sufficient discussion is found in [26] and [27], which also includes a new method of extracting the saliency based on the irregularity of the intensity of the region. In this method, some statistical measures are used to measure the irregularity of the region.…”

“…Regions with high irregularity measure are considered to be salient regions. In this research, we shall adopt Irregularity-Based Saliency given in [26] and [27] as a measure to extract the salient regions of an image.…”

Two-stage salient object identification and segmentation based on irregularity

“…[26]. Also, we shall use the exclusive OR measure (XOR) that was proposed in [6] and [9] and is calculated as given in Eq. ( 4).…”

“…Since in regular regions the expected value ij is very close to the pixels values and the standard deviation value ij is small, then the irregularity measure has been derived from these two measures as shown in Eq. ( 9) [6,9]:…”

“…Referring to our previous work in this field, it has been proven that irregularity can be used as a reliable measure in extracting saliency, which we will adopt in this study [6] and [9]. In this paper we shall use the principles of irregularity, both globally and locally, to develop a new robust algorithm for saliency extraction.…”