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

From Images to Schemas

International audienceIn Content Based Image Retrieval (CBIR), images are segmented to synthesize image information. Among several characteristics like color or edges, texture is useful for segmenting. This paper proposes an intensive multiresolution approach to texture segmentation based on a wavelet transform. The technique delivers schematic descriptions of images. That is to say, it provides the main regions of interest (ROIs) according to image information. Firstly, the process divides images into 2 × 2 blocks. Then, it tracks texture through the multiresolution offered by the wavelet transform to form featuring vectors. Next, a K-means algorithm partitions the texture vector space into clusters. Finally, a connected component extraction delivers the image schema. keywords : CBIR, intensive schematization, texture, color, wavele

Face Detection Technique Based on Skin Color and Facial Features

Face detection is an essential first step in face recognition systems with the purpose of localizing and extracting the face region from the background. Apart from increasing the efficiency of face recognition systems, face detection technique also opens up the door of opportunity for application areas such as content based image retrieval, video encoding, video conferencing, crowd surveillance and intelligent human computer interfaces. This thesis presents the design of face detection approach which is capable of detecting human faces from complex background. A skin color modeling process is adopted for the face segmentation process. Image enhancement is then used to improve the face candidate before feeding to the face object classifier based on Modified Hausdroff distance. The results indicate that the system is able to detect human faces with reasonable accurac

Color Image Segmentation Using Generalized Inverted Finite Mixture Models By Integrating Spatial Information

In computer vision, image segmentation plays foundational role. Innumerable techniques, such as active contour, graph-cut-based, model-based, machine learning, and clustering-based methods have been proposed for tackling the image segmentation problem. But, none of them is universally applicable. Thus, the hunt for optimized and robust models for image segmentation is still under-process and also an open question. The challenges faced in image segmentation are the integration of spatial information, finding the exact number of clusters (M), and to segment the image smoothly without any inaccuracy specially in the presence of noise, a complex background, low contrast and, inhomogeneous intensity. The use of finite mixture model (FMMs) for image segmentation is very popular approach in the field of computer vision. The application of image segmentation using FMM ranges from automatic number plate recognition, content-based image retrieval, texture recognition, facial recognition, satellite imagery etc. Image segmentation using FMM undergoes some problems. FMM-based image segmentation considers neither spatial correlation among the peer pixels nor the prior knowledge that the adjacent pixels are most likely belong to the same cluster. Also, color images are sensitive to illumination and noise. To overcome these limitations, we have used three different methods for integrating spatial information with FMM. First method uses the prior knowledge of M. In second method, we have used Markov Random Field (MRF). Lastly, in third, we have used weighted geometric and arithmetic mean template. We have implemented these methods with inverted Dirichlet mixture model (IDMM), generalized inverted Dirichlet mixture model (GIDMM) and inverted Beta Liouville mixture model (IBLMM). For experimentation, the Berkeley 500 (BSD500) and MIT's Computational Visual Cognition Laboratory (CVCL) datasets are employed. Furthermore, to compare the image segmentation results, the outputs of IDMM, GIDMM, and IBLMM are compared with each other, using segmentation performance evaluation metrics

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

Texture image retrieval using fuzzy image subdivision.

Large image databases containing a wide variety of imagery are increasingly more common. Because of their diversity and size, they are often poorly indexed. As a result, new automated techniques which index and search images using visual image properties, such as color and texture, have emerged. Texture is used to charaterize image regions and requires the regions of an image to be identified prior to indexing. Two region extraction methods: subdivision and segmentation are used for this task, and are used to compute direct-match and object-based queries, respectively. Direct-match queries are less costly to compute, and indexing is done without user intervention, resulting in automated content-based image retrieval systems (CBIR). In this thesis, we investigate the use of a new subdivision method, fuzzy image subdivision, to address the undesirable dependence on object position in direct-match texture queries. We implement our approach for querying images on the web and compare the retrieval performance with the current direct-match texture method based on rectangular partitioning, which does not take into account changes in object position. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1998 .I53. Source: Masters Abstracts International, Volume: 39-02, page: 0526. Adviser: Joan Morrissey. Thesis (M.Sc.)--University of Windsor (Canada), 1998

Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project

The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system