Muwei Jian

Visual-Patch-Attention-Aware Saliency Detection

The human visual system (HVS) can reliably perceive salient objects in an image, but, it remains a challenge to computationally model the process of detecting salient objects without prior knowledge of the image contents. This paper proposes a visual-attention-aware model to mimic the HVS for salient-object detection. The informative and directional patches can be seen as visual stimuli, and used as neuronal cues for humans to interpret and detect salient objects. In order to simulate this process, two typical patches are extracted individually and in parallel from the intensity channel and the discriminant color channel, respectively, as the primitives. In our algorithm, an improved wavelet-based salient-patch detector is used to extract the visually informative patches. In addition, as humans are sensitive to orientation features, and as directional patches are reliable cues, we also propose a method for extracting directional patches. These two different types of patches are then combined to form the most important patches, which are called preferential patches and are considered as the visual stimuli applied to the HVS for salient-object detection. Compared with the state-of-the-art methods for salient-object detection, experimental results using publicly available datasets show that our produced algorithm is reliable and effective.

Image retrieval using wavelet-based salient regions

Abstract A content-based image retrieval system normally returns the retrieval results according to the similarity between features extracted from the query image and candidate images. In certain circumstances, however, users may concern more about salient regions in an image of their interest and only wish to retrieve images containing the relevant salient regions while ignoring those irrelevant (such as the background or other regions and objects). Although how to represent the local image properties is still one of the most active research issues, much previous work on image retrieval does not examine salient regions in an image. In this paper, we propose an improved salient point detector based on wavelet transform; it can extract salient points in an image more accurately. Then salient points are segmented into different salient regions according to their spatial distribution. Colour moments and Gabor features of these different salient regions are computed and form a feature vector to index the image. We test the proposed scheme using a wide range of image samples from the Corel Image Library. The experimental results indicate that the method has produced promising results.

Image Fusion Based on Wavelet Transform

Image fusion is the process of combing multiple images of the same scene into a single fused image with the aim of preserving the full content information and retaining the important features from each of the original images. In this paper, we propose a novel scheme to measure every wavelet decomposition coefficients saliency of the original images. The saliency value reflects the visually meaningful content of the wavelet decomposition coefficients and is consistent with human visual perception. The novel scheme aims to preserve the full content value and retain the visually meaningful information with human visual perception more exactly than the traditional method. In addition, the proposed novel method can be combined with any sophisticated fusion rules and fusion operators that are based on wavelet decomposition. Experimental results show the effectiveness of the proposed scheme, which can retain perceptually important image information.

Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition

In video surveillance, the captured face images are usually of low resolution (LR). Thus, a framework based on singular value decomposition (SVD) for performing both face hallucination and recognition simultaneously is proposed in this paper. Conventionally, LR face recognition is carried out by super-resolving the LR input face first, and then performing face recognition to identify the input face. By considering face hallucination and recognition simultaneously, the accuracy of both the hallucination and the recognition can be improved. In this paper, singular values are first proved to be effective for representing face images, and the singular values of a face image at different resolutions have approximately a linear relation. In our algorithm, each face image is represented using SVD. For each LR input face, the corresponding LR and high-resolution (HR) face-image pairs can then be selected from the face gallery. Based on these selected LR-HR pairs, the mapping functions for interpolating the two matrices in the SVD representation for the reconstruction of HR face images can be learned more accurately. Therefore, the final estimation of the high-frequency details of the HR face images will become more reliable and effective. The experimental results demonstrate that our proposed framework can achieve promising results for both face hallucination and recognition.

Texture Image Classification Using Visual Perceptual Texture Features and Gabor Wavelet Features

Texture can describe a wide variety of surface characteristics and a key component for human visual perception and plays an important role in image-related applications. This paper proposes a scheme for texture image classification using visual perceptual texture features and Gabor wavelet features. Three new texture features which are proved to be in accordance with human visual perceptions are introduced. Usually, Subband statistics based on Gabor wavelet features are normally used to construct feature vectors for texture image classification. However, most previous methods make no further analysis of the decomposed subbands or simply remove most detail coefficients. The classification algorithms commonly use many features without consideration of whether the features are effective for discriminating different classes. This may produce unnecessary computation burden and even decrease the retrieval performance. This paper proposes a method for selecting effective Gabor wavelet subbands based on feature selection functions. The method can discard those subbands that are redundant or may lead to wrong classification results. We test our proposed method using the Brodatz texture database, and the experimental results show the scheme has produced promising results.

A novel face-hallucination scheme based on singular value decomposition

Abstract In this paper, an efficient mapping model based on singular value decomposition (SVD) is proposed for face hallucination. We can observe and prove that the main singular values of an image at one resolution have approximately linear relationships with their counterparts at other resolutions. This makes the estimation of the singular values of the corresponding high-resolution (HR) face images from a low-resolution (LR) face image more reliable. From the signal-processing point of view, this can effectively preserve and reconstruct the dominant information in the HR face images. Interpolating the other two matrices obtained from the SVD of the LR image does not change either the primary facial structure or the pattern of the face image. The corresponding two matrices for the HR face images can be constructed in a “coarse-to-fine” manner using global reconstruction. Our proposed method retains the holistic structure of face images, while the learned mapping matrices, which are represented as embedding coefficients of the individual mapping matrices learned from LR-HR training pairs, can be seen as holistic constraints in the reconstruction of HR images. Compared to state-of-the-art algorithms, experiments show that our proposed face-hallucination scheme is effective in terms of producing plausible HR images with both a holistic structure and high-frequency details.

Combining Color, Texture and Region with Objects of User's Interest for Content-Based Image Retrieval

Content-based image retrieval (CBIR) systems normally return the retrieval results according to the similarity between features extracted from the query image and candidate images. In certain circumstance, however, users concern more about objects of their interest and only wish to retrieve images containing relevant objects, while ignoring irrelevant image areas (such as the background). Previous work on retrieval of objects of users interest (OUT) normally requires complicated segmentation of the object from the background. In this paper, we propose a method that utilize color, texture and shape features of a user specified window containing the OUI to retrieve relevant images, whereas complicated image segmentation is avoided. We use color moments and subband statistics of wavelet decomposition as color and texture features respectively. The similarity is first calculated using these features. Then shape features, generated by mathematical morphology operators, are further employed to produce the final retrieval results. We use a wide range of color images for the experiments and evaluate the performance of the proposed method in different color spaces, including RGB, HSV, YCbCr. Although simple, the method has produced promising results.

Fast communication: Face-image retrieval based on singular values and potential-field representation

In this paper, an efficient method based on singular values and potential-field representation is proposed for face-image retrieval. Firstly, we theoretically prove that the leading singular values of an image can be used as a rotation-shift-scale-invariant global feature. Then, for the feature-extraction stage, we exploit these special properties of the singular values to devise a compact, global feature for face-image representation. We also use the singular values of the potential field derived from edge gradients to enhance the retrieval performance. Experimental results based on the GTAV database show that the use of singular values as rotation-shift-scale-invariant global features is able to produce plausible retrieval results.

Wavelet-Based Salient Regions and their Spatial Distribution for Image Retrieval

In content-based image retrieval, the representation of local properties in an image is one of the most active research issues. This paper proposes a salient region detector based on wavelet transform. The detector can extract the visually meaningful regions on an image and reflect local characteristics. An annular segmentation algorithm based on the distribution of salient regions is designed. It takes not only local image features into account, but also the spatial distribution information of the salient regions. Color moments and Gabor features around the salient regions in every annular region are computed as feature vectors used for indexing the image. We have tested the proposed scheme using a wide range of image samples from the Corel Image Library for content-based image retrieval. The experiments indicate that the method has produced promising results.

Facial-feature detection and localization based on a hierarchical scheme

An efficient hierarchical scheme, which is robust to illumination and pose variations in face images, is proposed for accurate facial-feature detection and localization. In our algorithm, having detected a face region using a face detector, a wavelet-based saliency map - which can reflect the most visually meaningful regions - is computed on the detected face region. As the eye region always has the most variations in a face image, the coarse eye region can be reliably located based on the saliency map, and verified by means of principal component analysis. This step in the proposed hierarchical scheme narrows down the search space, thereby reducing the computational cost in the further precise localization of the two eye positions based on a pose-adapted eye template. Moreover, among the facial features, the eyes play the most important role, and their positions can be used as an approximate geometric reference to localize the other facial features. Therefore, localization of the nose and mouth can be determined by using the saliency values in the saliency map and the detected eye positions as geometric references. Our proposed algorithm is non-iterative and computationally simple. Experimental results show that our algorithm can achieve a superior performance compared to other state-of-the-art methods.