Wavelet-Based Color Histogram on Content-Based Image Retrieval

The growth of image databases in many domains, including fashion, biometric, graphic design, architecture, etc. has increased rapidly. Content Based Image Retrieval System (CBIR) is a technique used for finding relevant images from those huge and unannotated image databases based on low-level features of the query images. In this study, an attempt to employ 2nd level Wavelet Based Color Histogram (WBCH) on a CBIR system is proposed. Image database used in this study are taken from Wang’s image database containing 1000 color images. The experiment results show that 2nd level WBCH gives better precision (0.777) than the other methods, including 1st level WBCH, Color Histogram, Color Co-occurrence Matrix, and Wavelet texture feature. It can be concluded that the 2nd Level of WBCH can be applied to CBIR system

HAPTIC VISUALIZATION USING VISUAL TEXTURE INFORMATION

Haptic enables users to interact and manipulate virtual objects. Although haptic research has influenced many areas yet the inclusion of computer haptic into computer vision, especially content based image retrieval (CBIR), is still few and limited. The purpose of this research is to design and validate a haptic texture search framework that will allow texture retrieval to be done not just visually but also haptically. Hence, this research is addressing the gap between the computer haptic and CBIR fields. In this research, the focus is on cloth textures. The design of the proposed framework involves haptic texture rendering algorithm and query algorithm. The proposed framework integrates computer haptic and content based image retrieval (CBIR) where haptic texture rendering is performed based on extracted cloth data. For the query purposes, the data are characterized and the texture similarity is calculated. Wavelet decomposition is utilized to extract data information from texture data. In searching process, the data are retrieved based on data distribution. The experiments to validate the framework have shown that haptic texture rendering can be performed by employing techniques that involve either a simple waveform or visual texture information. While rendering process was performed instability forces were generated during the rendering process was due to the limitation of the device. In the query process, accuracy is determined by the number of feature vector elements, data extraction, and similarity measurement algorithm. A user testing to validate the framework shows that users’ perception of haptic feedback differs depending on the different type of rendering algorithm. A simple rendering algorithm, i.e. sine wave, produces a more stable force feedback, yet lacks surface details compared to the visual texture information approach

Content Based Image Retrieval Based on Shape, Color and Structure of the Image

In the recent era, as technology is growing rapidly the usage of social media is also increasing as a result large databases are required for storing the images. With the advancements in the technology, the storage of these images in computers has become possible. But retrieving the images is becoming a big task. We need to store them in a sequential manner and retrieve them when required. This paper details retrieval of images by considering the features related to content like shape, color, texture is called CBIR (content based image retrieval). As it is very difficult to extract the pictures in such huge data bases so we chose this technique which aim at high efficiency

Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together

A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

This paper presents an integrated system for the breast cancer detection from mammograms based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist’s mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application

A Review Paper Based on Content-Based Image Retrieval

The quantity and complexity of digital image data is rapidly expanding. The user does not meet the demands of traditional information recovery technology, so an efficient system for content-based image collection must be developed. The image recovery from material becomes a source of reliable and rapid recovery. In this paper, characteristics such as color correlogram, texture, form, edge density are compared. For understanding and acquiring much better knowledge on a specific subject, literature surveys are most relevant. In this paper, we discuss some technical aspects of the current image recovery systems based on content

A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

With the advancement of digital storing and capturing technologies in recent years, an image retrieval system has been widely known for Internet usage. Several image retrieval methods have been proposed to find similar images from a collection of digital images to a specified query image. Content-based image retrieval (CBIR) is a subfield of image retrieval techniques that extracts features and descriptions content such as color, texture, and shapes from a huge database of images. This paper proposes a two-tier image retrieval approach, a coarse matching phase, and a fine-matching phase. The first phase is used to extract spatial features, and the second phase extracts texture features based on the Slantlet transform. The findings of this study revealed that texture features are reliable and capable of producing excellent results and unsusceptible to low resolution and proved that the SLT-based texture feature is the perfect mate. The proposed method\u27s experimental results have outperformed the benchmark results with precision gaps of 28.0 % for the Caltech 101 dataset. The results demonstrate that the two-tier strategy performed well with the successive phase (fine-matching) and the preceding phase (coarse matching) working hand in hand harmoniously

An Intelligent Multi-Resolutional and Rotational Invariant Texture Descriptor for Image Retrieval Systems

To find out the identical or comparable images from the large rotated databases with higher retrieval accuracy and lesser time is the challenging task in Content based Image Retrieval systems (CBIR). Considering this problem, an intelligent and efficient technique is proposed for texture based images. In this method, firstly a new joint feature vector is created which inherits the properties of Local binary pattern (LBP) which has steadiness regarding changes in illumination and rotation and discrete wavelet transform (DWT) which is multi-resolutional and multi-oriented along with higher directionality. Secondly, after the creation of hybrid feature vector, to increase the accuracy of the system, classifiers are employed on the combination of LBP and DWT. The performance of two machine learning classifiers is proposed here which are Support Vector Machine (SVM) and Extreme learning machine (ELM). Both proposed methods P1 (LBP+DWT+SVM) and P2 (LBP+DWT+ELM) are tested on rotated Brodatz dataset consisting of 1456 texture images and MIT VisTex dataset of 640 images. In both experiments the results of both the proposed methods are much better than simple combination of DWT +LBP and much other state of art methods in terms of precision and accuracy when different number of images is retrieved.  But the results obtained by ELM algorithm shows some more improvement than SVM. Such as when top 25 images are retrieved then in case of Brodatz database the precision is up to 94% and for MIT VisTex database its value is up to 96% with ELM classifier which is very much superior to other existing texture retrieval methods

Content Based Image Retrieval by Using Interactive Relevance Feedback Technique - A Survey

Due to rapid increase in storing and capturing multimedia data with the digital device, Content Based Image Retrieval play a very important role in the field of image processing. Although wide ranging studies have been done in the field of CBIR but image finding from multimedia data basis is still very complicated and open problem. If paper provide an review of CBIR based on some of the famous techniques such as Interactive Genetic Algorithm, Relevance Feedback (RS), Neural Network and so on. Relevance Feedback can be used to enhance the ability of CBIR effectively by dropping the semantic gap between low level feature and high level feature. Interactiveness on CBIR can also be done with the help of Genetic Algorithms. GA is the branch of evolutionary computation which makes the retrieval process more interactive so that user can get advanced results from database by comparing to Query Image with its evaluation. The result of traditional implicit feedback can also be improved by Neuro Fuzzy Logic based implicit feedback. This paper covers all the aspect of Relevance Feedback (RF), Interactive Genetic Algorithms, Neural Network in Content Based Image Retrieval, various RF techniques and applications of CBIR. DOI: 10.17762/ijritcc2321-8169.15075