Neural network-based shape retrieval using moment invariants and Zernike moments.

Shape is one of the fundamental image features for use in Content-Based Image Retrieval (CBIR). Compared with other visual features such as color and texture, it is extremely powerful and provides capability for object recognition and similarity-based image retrieval. In this thesis, we propose a Neural Network-Based Shape Retrieval System using Moment Invariants and Zernike Moments. Moment Invariants and Zernike Moments are two region-based shape representation schemes and are derived from the shape in an image and serve as image features. k means clustering is used to group similar images in an image collection into k clusters whereas Neural Network is used to facilitate retrieval against a given query image. Neural Network is trained by the clustering result on all of the images in the collection using back-propagation algorithm. In this scheme, Neural Network serves as a classifier such that moments are inputs to the Neural Network and the output is one of the k classes that have the largest similarities to the query image. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C444. Source: Masters Abstracts International, Volume: 44-03, page: 1396. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Image-Based Query by Example Using MPEG-7 Visual Descriptors

This project presents the design and implementation of a Content-Based Image Retrieval (CBIR) system where queries are formulated by visual examples through a graphical interface. Visual descriptors and similarity measures implemented in this work followed mainly those defined in the MPEG-7 standard although, when necessary, extensions are proposed. Despite the fact that this is an image-based system, all the proposed descriptors have been implemented for both image and region queries, allowing the future system upgrade to support region-based queries. This way, even a contour shape descriptor has been developed, which has no sense for the whole image. The system has been assessed on different benchmark databases; namely, MPEG-7 Common Color Dataset, and Corel Dataset. The evaluation has been performed for isolated descriptors as well as for combinations of them. The strategy studied in this work to gather the information obtained from the whole set of computed descriptors is weighting the rank list for each isolated descriptor

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding followed by morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture feature vectors is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.Comment: 7 page

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

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Representing and retrieving regions using binary partition trees

This paper discusses the interest of Binary Partition Trees for image and region representation in the context of indexing and similarity based retrieval. Binary Partition Trees concentrate in a compact and structured way the set of regions that compose an image. Since the tree is able to represent images in a multiresolution way, only simple descriptors need to be attached to the nodes. Moreover, this representation is used for similarity based region retrieval.Peer ReviewedPostprint (published version