Investigation on advanced image search techniques

Content-based image search for retrieval of images based on the similarity in their visual contents, such as color, texture, and shape, to a query image is an active research area due to its broad applications. Color, for example, provides powerful information for image search and classification. This dissertation investigates advanced image search techniques and presents new color descriptors for image search and classification and robust image enhancement and segmentation methods for iris recognition. First, several new color descriptors have been developed for color image search. Specifically, a new oRGB-SIFT descriptor, which integrates the oRGB color space and the Scale-Invariant Feature Transform (SIFT), is proposed for image search and classification. The oRGB-SIFT descriptor is further integrated with other color SIFT features to produce the novel Color SIFT Fusion (CSF), the Color Grayscale SIFT Fusion (CGSF), and the CGSF+PHOG descriptors for image category search with applications to biometrics. Image classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbor (KNN) decision rule. Experimental results on four large scale, grand challenge datasets have shown that the proposed oRGB-SIFT descriptor improves recognition performance upon other color SIFT descriptors, and the CSF, the CGSF, and the CGSF+PHOG descriptors perform better than the other color SIFT descriptors. The fusion of both Color SIFT descriptors (CSF) and Color Grayscale SIFT descriptor (CGSF) shows significant improvement in the classification performance, which indicates that various color-SIFT descriptors and grayscale-SIFT descriptor are not redundant for image search. Second, four novel color Local Binary Pattern (LBP) descriptors are presented for scene image and image texture classification. Specifically, the oRGB-LBP descriptor is derived in the oRGB color space. The other three color LBP descriptors, namely, the Color LBP Fusion (CLF), the Color Grayscale LBP Fusion (CGLF), and the CGLF+PHOG descriptors, are obtained by integrating the oRGB-LBP descriptor with some additional image features. Experimental results on three large scale, grand challenge datasets have shown that the proposed descriptors can improve scene image and image texture classification performance. Finally, a new iris recognition method based on a robust iris segmentation approach is presented for improving iris recognition performance. The proposed robust iris segmentation approach applies power-law transformations for more accurate detection of the pupil region, which significantly reduces the candidate limbic boundary search space for increasing detection accuracy and efficiency. As the limbic circle, which has a center within a close range of the pupil center, is selectively detected, the eyelid detection approach leads to improved iris recognition performance. Experiments using the Iris Challenge Evaluation (ICE) database show the effectiveness of the proposed method

Novel color and local image descriptors for content-based image search

Content-based image classification, search and retrieval is a rapidly-expanding research area. With the advent of inexpensive digital cameras, cheap data storage, fast computing speeds and ever-increasing data transfer rates, millions of images are stored and shared over the Internet every day. This necessitates the development of systems that can classify these images into various categories without human intervention and on being presented a query image, can identify its contents in order to retrieve similar images. Towards that end, this dissertation focuses on investigating novel image descriptors based on texture, shape, color, and local information for advancing content-based image search. Specifically, first, a new color multi-mask Local Binary Patterns (mLBP) descriptor is presented to improve upon the traditional Local Binary Patterns (LBP) texture descriptor for better image classification performance. Second, the mLBP descriptors from different color spaces are fused to form the Color LBP Fusion (CLF) and Color Grayscale LBP Fusion (CGLF) descriptors that further improve image classification performance. Third, a new HaarHOG descriptor, which integrates the Haar wavelet transform and the Histograms of Oriented Gradients (HOG), is presented for extracting both shape and local information for image classification. Next, a novel three Dimensional Local Binary Patterns (3D-LBP) descriptor is proposed for color images by encoding both color and texture information for image search. Furthermore, the novel 3DLH and 3DLH-fusion descriptors are proposed, which combine the HaarHOG and the 3D-LBP descriptors by means of Principal Component Analysis (PCA) and are able to improve upon the individual HaarHOG and 3D-LBP descriptors for image search. Subsequently, the innovative H-descriptor, and the H-fusion descriptor are presented that improve upon the 3DLH descriptor. Finally, the innovative Bag of Words-LBP (BoWL) descriptor is introduced that combines the idea of LBP with a bag-of-words representation to further improve image classification performance. To assess the feasibility of the proposed new image descriptors, two classification frameworks are used. In one, the PCA and the Enhanced Fisher Model (EFM) are applied for feature extraction and the nearest neighbor classification rule for classification. In the other, a Support Vector Machine (SVM) is used for classification. The classification performance is tested on several widely used and publicly available image datasets. The experimental results show that the proposed new image descriptors achieve an image classification performance better than or comparable to other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (ScSPM), the Kernel Codebook (KC) and the LBP

Investigation of new feature descriptors for image search and classification

Content-based image search, classification and retrieval is an active and important research area due to its broad applications as well as the complexity of the problem. Understanding the semantics and contents of images for recognition remains one of the most difficult and prevailing problems in the machine intelligence and computer vision community. With large variations in size, pose, illumination and occlusions, image classification is a very challenging task. A good classification framework should address the key issues of discriminatory feature extraction as well as efficient and accurate classification. Towards that end, this dissertation focuses on exploring new image descriptors by incorporating cues from the human visual system, and integrating local, texture, shape as well as color information to construct robust and effective feature representations for advancing content-based image search and classification. Based on the Gabor wavelet transformation, whose kernels are similar to the 2D receptive field profiles of the mammalian cortical simple cells, a series of new image descriptors is developed. Specifically, first, a new color Gabor-HOG (GHOG) descriptor is introduced by concatenating the Histograms of Oriented Gradients (HOG) of the component images produced by applying Gabor filters in multiple scales and orientations to encode shape information. Second, the GHOG descriptor is analyzed in six different color spaces and grayscale to propose different color GHOG descriptors, which are further combined to present a new Fused Color GHOG (FC-GHOG) descriptor. Third, a novel GaborPHOG (GPHOG) descriptor is proposed which improves upon the Pyramid Histograms of Oriented Gradients (PHOG) descriptor, and subsequently a new FC-GPHOG descriptor is constructed by combining the multiple color GPHOG descriptors and employing the Principal Component Analysis (PCA). Next, the Gabor-LBP (GLBP) is derived by accumulating the Local Binary Patterns (LBP) histograms of the local Gabor filtered images to encode texture and local information of an image. Furthermore, a novel Gabor-LBPPHOG (GLP) image descriptor is proposed which integrates the GLBP and the GPHOG descriptors as a feature set and an innovative Fused Color Gabor-LBP-PHOG (FC-GLP) is constructed by fusing the GLP from multiple color spaces. Subsequently, The GLBP and the GHOG descriptors are then combined to produce the Gabor-LBP-HOG (GLH) feature vector which performs well on different object and scene image categories. The six color GLH vectors are further concatenated to form the Fused Color GLH (FC-GLH) descriptor. Finally, the Wigner based Local Binary Patterns (WLBP) descriptor is proposed that combines multi-neighborhood LBP, Pseudo-Wigner distribution of images and the popular bag of words model to effectively classify scene images. To assess the feasibility of the proposed new image descriptors, two classification methods are used: one method applies the PCA and the Enhanced Fisher Model (EFM) for feature extraction and the nearest neighbor rule for classification, while the other method employs the Support Vector Machine (SVM). The classification performance of the proposed descriptors is tested on several publicly available popular image datasets. The experimental results show that the proposed new image descriptors achieve image search and classification results better than or at par with other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Context Aware Topic Model (CA-TM), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (Sc-SPM), the Kernel Codebook (KC) and the LBP

Evaluating color texture descriptors under large variations of controlled lighting conditions

The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed on a new texture database including 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction and intensity. The database allows to systematically investigate the robustness of texture descriptors across a large range of variations of imaging conditions.Comment: Submitted to the Journal of the Optical Society of America