Divergence Filter for Saliency

2015 Seventh International Conference on Knowledge and Systems Engineering

DIVERGENCE FILTER FOR SALIENCY Dao Nam Anh Department of Information Technology Electric Power University Hanoi, Vietnam

a) Input image

b) Salient region

c) Object extraction

Fig. 1. Saliency estimation by the addaptive bilateral filter

[1]. Traditional approach for the problem is the homogeneous analysis on basic features such as color and depth. Given the utility of the saliency detection approaches, various applications are addressed allowing image compression [2], image retrieval [3], image retargeting [4], recognition of objects [5], image forensics [6], image rendering, cropping and mosaic [7].

Abstract — Detection of regions with high visual attention from image has various applications including advertising design where ads are often associated with relevant semantic visual information. The salient regions in the image/video have to be identified in a consistent way, even if original objects or background are texture scene. This is achieved by solving combinatorial problem of down-sampling that searches for the optimal texture region map. The complexity of this solution makes it impractical. The problem becomes easy by a new approach for saliency detection. It is based on the spatial attention model that evaluates divergence of a given local region from its surrounding where objects and background can be texture scene.

Uncovering such salient objects would need complexity of tasks including combinatorial solution. In particular, it is time consuming to detect the salient region from textured object and background. Fundamentally, both saliency and segmentation strive to construct a representation of the similarity. Thus, the most feasible way for detecting salient region is related to segmentation [8]. The approach is established on luminance and color [9], isocentric curvedness and color [10], regional contrast [11], learning and graph cut refinement [12]. The psychophysical stimuli method [13] combines information across modalities including orientation, intensity and color information for searching salient region.

Our proposed solution is based on an adaptive version of the bilateral filter that searches for the divergence of a pixel with its local neighbors. The contributions of this work are new divergence estimation function which reduces potential global search into a simple local filter, and efficient convex-hull algorithm for creating saliency map. Experimental results show that the solution can deal with texture during analysis of visual attention, and saliency detection’s performance is improved.

This work proposes a new algorithm that estimates spacial attention by local divergence analysis. The solution is derived from the bilateral filter [14][15]. A new adaptive version of the filter is designed for the divergence estimation of a pixel to its neighbors. Though the uniform Gaussian window is used to get local similarity, a new divergence function is derived to predict saliency of region according to their contrast properties. The filter is applied to the image color and gradients to uncover texture information that may be hidden and confused with edges.

Keywords — bilateral filter, saliency detection, texture scence, convex hulls

I. INTRODUCTION Transmitting a picture into a saliency map – enables a user to select region with unequal attention from which to perform semantic analysis of the region and to suggest relevant actions. This ability requires detection of visual saliency that was considered as the prediction of eye-fixations on images. Furthermore, it is extended into the identification of salient object in a perceptually plausible way and respecting the most significant content from extensive image/video frame.

The approach turns the global saliency-based search into uncomplicated filter reducing computational complexity. Further, saliency map is produced with convex hulls to export salient region. So the bilateral filter is required to carefully estimate local divergence. Experimental results show outperformance of the algorithm in F-measure for the saliency benchmark [16]. See Fig.1 for an illustration. The input texture scene in Fig.1a holds textured object (squirrel) and background

For instance, when designing ads for an online streaming video frame, it is often necessary to allocate the region having the most visual attention and uncover meaning of the region. This allows to select ads relevant to the semantic information

978-1-4673-8013-3/15 $31.00 © 2015 IEEE DOI 10.1109/KSE.2015.8

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saliency estimation that are formulated in a consolidated way using high-dimensional Gaussian filters. The contrast measures weight the uniqueness and the spatial distribution of these elements while saliency map consistently presents the objects of interest.

(sand). The filter produces saliency map in Fig.1b, followed by extracted salient region in Fig.1b made by the convex hulls. The structure of the paper is as follows: in Sect. 2, techniques of saliency detection and bilateral filter are reviewed; in Sect. 3 an adaptive bilateral filter is described on the relationship to divergence estimation; Sect. 4 presents our saliency algorithm to support the findings of Sect. 3; experiments and discussions are collected in Sect. 5, 6; future work and concluding remarks are made in Sect. 7, 8.

Given perceptually uniform CIELab space, a salient-object detection method firstly deals with the spatial center and variances of the quantized colors via BF to produce a probability of saliency based on a statistical object model [20]. Then, the global color contrast is computed and final saliency map is exported. The inverse bilateral filter is addressed in [23] that allows the system to output saliency maps with salient objects in their context. The filter is described firstly for learning local contrast distribution to predict salient image regions. Local opposition is analyzed by the second application of the inverse bilateral filter to establish fuzzy boundary of salient regions in form of trimap.

II. PRIOR WORK Existing saliency detection algorithms are widely addressed in computer vision application. In particular, fundamental denoising operation with the bilateral filter (BF) [14] that allows keeping well shapes was improved though the saliency map. Thus, parameters of the BF in [17] are modified by the saliency. The parameters are geometric spread and weights in the intensity domain. The saliency map used here is detected by image color, intensity, and temporal changes between frames. Accordingly, video de-blocking is achieved by the adaptive bilateral filter. The method ameliorates the quality of highly compressed video sequences.

For instance, [24] proposed a method to determine salient regions in images using low-level features of luminance and color. The method generates high quality saliency maps supporting segmentation of semantically meaningful whole objects. Covariance descriptors based method [25] suggested to use covariance matrices of simple image features as metafeatures for saliency estimation. As low dimensional representations of image patches, region covariances capture local image structures better than standard linear filters.

Image diffusion by bilateral filter in [18] is proposed in connection with perceptual metrics. The BF’s filtering kernels are regulated by the saliency of boundaries, so that the adaptive BF is capable to remove noise and preserve salient boundaries. The performance of BF is augmented in [19] by flexible parameter modification. The standard deviation for geometric spread and photometric spread are dependent on the saliency. Smaller filter parameters are arranged in the regions of high saliency.

Above contrast-based saliency methods enable saliency detection usually need computing time for the contrast measurement or statistic task. Our work is concentrated on the detection of saliency with a specific adaptive version of the BF that delivers results without contrast measurement step or machine learning in reasonable time. Consequently, next Sect. outlines concept of the work.

Further, a video coding scheme implements the technique of visual saliency estimation to regulate image fidelity before compression [20]. Visual salient features in format of spatiotemporal saliency map is constructed by investigating the video using a joined bottom-up and top-down visual saliency model. Then BF is adapted, where the local intensity and spatial scales are modified according to visual saliency, to adaptively alter the image fidelity. The BF can raise up the compression ratio significantly while retaining perceptual visual quality.

III. ADAPTIVE BILATERAL FILTER FOR SALIENCY For notational simplicity, the vectored notations are used for denoting indices of spatial pixels x and image color is denoted by function u:

Content-aware saliency detection in [21] considers color difference between the patch centered at the current pixel and the patches centered at its neighboring pixels. The image histogram is integrated into the saliency detection to keep the accuracy. A saliency guided stylized rendering scheme is given by incorporating the saliency regulated BF and saliency adapted contour detection.

u( x ) : Ω → ℜ3 , Ω ∈ ℜ 2

(1)

Here  is the index set for the entire image. The salient map s(x) and object location o(x) are necessary to detect: s ( x ) : Ω → ℜ1

o( x ) : Ω → {true, false}, o( x ) = true, if s ( x ) > t

As was previously mentioned, above works addressed to the improvement for BF by saliency map where BF’s parameters are adjusted by the saliency estimation. Major objective of the works is to improve quality of image through BF which parameters are regulated by saliency map. This is the ways of saliency application but not for finding saliency map. Further review looks for study in a back way - application of BF for saliency detection.

(2) (3)

where t is salient threshold. The Gaussian filter with deviation  for pixel x checks its neighborhood Nτ (x ) in a frame with size  : Gσ ( x) =

An automatic algorithm for contrast-based saliency estimation is presented in [22]. It consists of contrast and

§ x2 · 1 exp¨¨ − 2 ¸¸ σ 2π © 2σ ¹

N τ ( x ) = { y , y ∈ Ω , y − x