Content-Based Image Retrieval through Improved Subblock Technique

Traditional Content-Based Image Retrieval (CBIR) systems mainly relied on the extraction of features globally. The drawback of this approach is that it cannot sufficiently capture the important features of individual regions in an image which users might be interested in. Due to that, an extension of the CBIR systems is designed to exploit images at region or object level. One of the important tasks in CBIR at region or object level is to segment images into regions based on low-level features. Among the low-level features, colour and location information are widely used. In order to extract the colour information, Colour-based Dominant Region segmentation is used to extract a maximum of three dominant colour regions in an image together with its respective coordinates of the Minimum-Bounding Rectangle (MBR). The Sub-Block technique is then used to determine the location of the dominant regions by comparing the coordinates of the region’s MBR with the four corners of the centre of the location map. The cell number that is maximally covered by the region is supposedly to be assigned as the location index. However, the Sub- Block technique is not reliable because in most cases, the location index assigned is not the cell number that is maximally covered by the region and sometimes a region does not overlap with the cell number assigned at all. The effectiveness of this technique has been improved by taking into consideration the total horizontal and vertical distance of a region at each location where it overlaps. The horizontal distance from the left edge to the right edge of a region and the vertical distance from the top edge to the bottom edge of a region are calculated. The horizontal and vertical distances obtained for that region are then counted. The cell number with the highest distance would be assigned as the location index for that region. The individual colour and location index of each dominant region in an image is extended to provide combined colour-spatial indexes. During retrieval, images in the image database that have the same index as the query image is retrieved. A CBIR system implementing the Improved Sub-Block technique is developed. The CBIR system supports Query-By-Example (QBE). The retrieval effectiveness of the improved technique is tested through retrieval experiments on six image categories of about 900 images. The precision and recall is measured. From the experiments it is shown that retrieval effectiveness has been significantly improved by 85.86% through the Improved Sub-Block technique

Content-based fauna image retrieval system

Many animal species exist in this world and there are always new species being discovered each year. Therefore, it is very important that these valuable species be documented properly to be referred to in future. Numerous information retrieval systems for managing and documenting animal species today only allow users to search animal images and descriptions online via text-based input. Therefore, people without knowledge on the animal species or without Internet access are not able to search using the systems. Motivated by these issues, the focus of this work is to construct a colour-shape content-based image representation for fauna. Two orders of the Colour Moment are used to represent the colour feature while the i-means approach is used to represent the shape feature. Based on the conducted quantitative and qualitative studies, the proposed fusion method together with the Content-based Image Retrieval (CBIR) system are found to be very effective in retrieving animal images similar to the given query, able to provide reliable and useful information on animal species, an easy system to interact with, and has easy to understand and user-friendly interfaces

A review on content-based image retrieval representation and description for fish

There is an increasing interest in the description and representation of fish species images. For that purpose, Content-based Image Retrieval (CBIR) is applied. Due to the uncontrolled deep sea underwater environment, it is very hard to accurately estimate the similarities between the fishes and retrieves them according to its species due to ineffective visual features extraction for fish image representation. In this paper, CBIR for representation and description of fish is reviewed. Shape is one of the most important features to describe fish. This paper considers the combination of global and local shape features. Existing combination is carefully studied and the importances of global and local shape features are presented. The focus of possible future works is also suggested

Multi-resolution shape-based image retrieval using Ridgelet transform

Complicated shapes can be effectively characterized using multi-resolution descriptors. One popular method is the Ridgelet transform which has enjoyed very little exposure in describing shapes for Content-based Image Retrieval (CBIR). Many of the existing Ridgelet transforms are only applied on images of size M×M. For M×N sized images, they need to be segmented into M×M sub-images prior to processing. A different number of orientations and cut-off points for the Radon transform parameters also need to be utilized according to the image size. This paper presents a new shape descriptor for CBIR based on Ridgelet transform which is able to handle images of various sizes. The utilization of the ellipse template for better image coverage and the normalization of the Ridgelet transform are introduced. For better retrieval, a template-option scheme is also introduced. Retrieval effectiveness obtained by the proposed method has shown to be higher compared to several previous descriptors

Content-based image retrieval system for marine invertebrates

There are many marine life around the world where it is essential to have proper documentation for future records. Many information retrieval systems for marine science today require text input from user and can only be accessed online. Therefore, people who do not know the name of the marine species or do not have Internet access cannot search using the systems. Responding to this important need, this work aims to develop a Content-based Image Retrieval (CBIR) system for marine invertebrates based on colour and shape features. With the CBIR system for marine invertebrates, users can use the system to look for marine invertebrates' species instead of using traditional methods of searching such as using books and encyclopedias. Users can easily upload the image of marine invertebrate that they want to search into the system and the system will retrieve all the other similar images of marine invertebrates along with their description. All the system interface's buttons, icons and text were designed in a way where any user can easily understand and further learn to operate the system themselves. Based on the retrieval effectiveness experiment and questionnaire-based survey, the proposed CBIR system for marine invertebrates is shown to be effective, help users search similar images of marine invertebrates, provide concise information on marine invertebrate's species for learning purposes, and is reliable and user-friendly

Using concatenation cost for unit selection of homosonic segments in concatenative sound synthesis

This paper studies the issues surrounding the search and selection process in a general CSS system which may affect the synthesis result, namely the homosonic segments. Homosonic segments are first termed in this study, where it refers to audio files which have one or more of the same sonic properties with each other, but do not sound the same acoustically when played due to the limited audio features extracted during the analysis process. These homosonic segments create confusions within the CSS selection engine. This study proposes a robust solution to overcome this issue by introducing the concatenation cost in addition to the regular target cost. The experiment conducted in this study observes that the use of concatenation cost to help solve the problem is feasible. Further evaluation also suggests that the concatenation cost is an effective solution in solving the challenges involving homosonic segments as the sounds synthesised through concatenation cost function have a better accuracy and possess higher fluency when concatenated from one segment to the next

Content-based image retrieval using colour and shape fused features

Multi-feature methods are able to contribute to a more effective method compared to single-feature methods since feature fusion methods will be able to close the gap that exists in the single-feature methods. This paper presents a feature fusion method, which focuses on extracting colour and shape features for content-based image retrieval (CBIR). The colour feature is extracted based on the proposed Multi-resolution Joint Auto Correlograms (MJAC), while the shape information is obtained through the proposed Extended Generalised Ridgelet-Fourier (EGRF). These features are fused together through a proposed integrated scheme. The feature fusion method has been tested on the SIMPLIcity image database, where several retrieval measurements are utilised to compare the effectiveness of the proposed method with few other comparable methods. The retrieval results show that the proposed Integrated Colour-shape (ICS) descriptor has successfully obtained the best overall retrieval performance in all the retrieval measurements as compared to the benchmark methods, which include precision (53.50%), precision at 11 standard recall levels (52.48%), and rank (17.40)

Vid2U: facilitating students’ learning through video annotation for tablet device

Electronic learning (e-learning) applications are widely used in the Higher-Education institutions to deliver and share learning contents among the students. However, most of the e-learning applications are developed for offline or online desktops. Mobile learning (m-learning) is introduced to allow learners to experience the learning opportunities via mobile devices such as handheld computers, MP3 players, notebooks, mobile phones, and tablets. Interactive video refers to a technique which allows users to have some interactions with the media instead of watching a static video. One of the many ways of making video having the interactive elements is through video annotation. Although the use of video annotation is quite common in learning, there are still needs for more efforts on designing annotation facilities for video management in mobile learning environment. Apart from that, the application of video annotation on mobile devices is also still in its infancy, especially for recent devices like iPad, Microsoft Surface, Samsung Galaxy Tab, and others. The objective of the proposed mobile learning application, Vid2U is to provide the flexibility of accessing and managing video materials at anywhere and anytime, making learning even more widely available. The Vid2U is equipped with many functions such as allowing students to view lecture videos and other video materials related to the course when absent from classes. Students can also add, edit, and delete their lecture notes based on the lecture videos. The mobile-based application also allows students to search video materials related to all courses taken by them. Experimental results obtained from surveys have shown that the Vid2U is able to provide better learning environment for the university learners, which lead to a deeper level of learning engagement

Invariant Generalised Ridgelet-Fourier for shape-based image retrieval

A new shape descriptor called the Invariant Generalised Ridgelet-Fourier is defined for the application of Content-based Image Retrieval (CBIR). The proposed spectral-based method is invariant to rotation, scaling, and translation (RST) as well as able to handle images of arbitrary size. The implementation of Ridgelet transform on the ellipse containing the shape and the normalisation of the Radon transform is introduced. The 1D Wavelet transform is then applied to the Radon slices. In order to extract the rotation invariant feature, Fourier transform is implemented in the Ridgelet domain. The performance of the proposed method is accessed on a standard MPEG-7 CE-1 B dataset in terms of few objective evaluation criteria. From the experiments, it is shown that the proposed method provides promising results compared to several previous methods

Generalized Ridgelet-Fourier for M×N images: determining the normalization criteria

Ridgelet transform (RT) has gained its popularity due to its capability in dealing with line singularities effectively. Many of the existing RT however is only applied to images of size M×M or the M×N images will need to be pre-segmented into M×M sub-images prior to processing. The research presented in this article is aimed at the development of a generalized RT for content-based image retrieval so that it can be applied easily to any images of various sizes. This article focuses on comparing and determining the normalization criteria for Radon transform, which will aid in achieving the aim. The Radon transform normalization criteria sets are compared and evaluated on an image database consisting of 216 images, where the precision and recall and Averaged Normalized Modified Retrieval Rank (ANMRR) are measured