Region Based Image Retrieval Using Ratio of Proportional Overlapping Object

In Region Based Image Retrieval (RBIR), determination of the relevant block in query region is based on the percentage of image objects that overlap with each sub-blocks. But in some images, the size of relevant objects are small. It may cause the object to be ignored in determining the relevant sub-blocks. Therefore, in this study we proposed a system of RBIR based on the percentage of proportional objects that overlap with sub-blocks. Each sub-blocks is selected as a query region. The color and texture features of the query region will be extracted by using HSV histogram and Local Binary Pattern (LBP), respectively. We also used shape as global feature by applying invariant moment as descriptor. Experimental results show that the proposed method has average precision with 74%

A FLEXIBLE SUB-BLOCK IN REGION BASED IMAGE RETRIEVAL BASED ON TRANSITION REGION

One of the techniques in region based image retrieval (RBIR) is comparing the global feature of an entire image and the local feature of image’s sub-block in query and database image. The determined sub-block must be able to detect an object with varying sizes and locations. So the sub-block with flexible size and location is needed. We propose a new method for local feature extraction by determining the flexible size and location of sub-block based on the transition region in region based image retrieval. Global features of both query and database image are extracted using invariant moment. Local features in database and query image are extracted using hue, saturation, and value (HSV) histogram and local binary patterns (LBP). There are several steps to extract the local feature of sub-block in the query image. First, preprocessing is conducted to get the transition region, then the flexible sub-block is determined based on the transition region. Afterward, the local feature of sub-block is extracted. The result of this application is the retrieved images ordered by the most similar to the query image. The local feature extraction with the proposed method is effective for image retrieval with precision and recall value are 57%

Content based image retrieval for bio-medical images

Content Based Image Retrieval System (CBIR) is used to retrieve images similar to the query image. These systems have a wide range of applications in various fields. Medical subject headings, key words, and bibliographic references can be augmented with the images present within the articles to help clinicians to potentially improve the relevance of articles found in the querying process. In this research, image feature analysis and classification techniques are explored to differentiate images found in biomedical articles which have been categorized based on modality and utility. Examples of features examined in this research include: features based on different histograms of the image, texture features, fractal dimensions etc. Classification algorithms used for categorization were 1) Mean shift clustering 2) Radial basis clustering. Different combinations of features were selected for classification purposes and it was observed that features incorporating soft decision based HSV histogram features give the best results. A library of features was then developed which can be used in RapidMiner. Experimental results for various combinations of features have also been included --Abstract, page iii

Region-based Multimedia Indexing and Retrieval Framework

Many systems have been proposed for automatic description and indexing of digital data, for posterior retrieval. One of such content-based indexing-and-retrieval systems, and the one used as a framework in this thesis, is the MUVIS system, which was developed at Tampere University of Technology, in Finland. Moreover, Content-based Image Retrieval (CBIR) utilising frame-based and region-based features has been a dynamic research area in the past years. Several systems have been developed using their specific segmentation, feature extraction, and retrieval methods. In this thesis, a framework to model a regionalised CBIR framework is presented. The framework does not specify or fix the segmentation and local feature extraction methods, which are instead considered as “black-boxes” so as to allow the application of any segmentation method and visual descriptor. The proposed framework adopts a grouping approach in order to correct possible over- segmentation faults and a spatial feature called region proximity is introduced to describe regions topology in a visual scene by a block-based approach. Using the MUVIS system, a prototype system of the proposed framework is implemented as a region-based feature extraction module, which integrates simple colour segmentation and region-based feature description based on colour and texture. The spatial region proximity feature represents regions and describes their topology by a novel metric proposed in this thesis based on the block-based approach and average distance calculation. After the region-based feature extraction step, a feature vector is formed which holds information about all image regions with their local low-level and spatial properties. During the retrieval process, those feature vectors are used for computing the (dis-)similarity distances between two images, taking into account each of their individual components. In this case a many-to-one matching scheme between regions characterised by a similarity maximisation approach is integrated into a query-by-example scheme. Retrieval performance is evaluated between frame-based feature combination and the proposed framework with two different grouping approaches. Experiments are carried out on synthetic and natural image databases and the results indicate that a promising retrieval performance can be obtained as long as a reasonable segmentation quality is obtained. The integration of the region proximity feature further improves the retrieval performance especially for divisible, object-based image content. Finally, frame-based and region-based texture extraction schemes are compared to evaluate the effect of a region on the texture description and retrieval performance utilising the proposed framework. Results show that significant degradations over the retrieval performance occur on region-based texture descriptors compared with the frame-based approaches

Web Scale Image Retrieval based on Image Text Query Pair and Click Data

The growing importance of traditional text-based image retrieval is due to its popularity through web image search engines. Google, Yahoo, Bing etc. are some of search engines that use this technique. Text-based image retrieval is based on the assumption that surrounding text describes the image. For text-based image retrieval systems, input is a text query and output is a ranking set of images in which most relevant results appear first. The limitation of text-based image retrieval is that most of the times query text is not able to describe the content of the image perfectly since visual information is full of variety. Microsoft Research Bing Image retrieval Challenge aims to achieve cross-modal retrieval by ranking the relevance of the query text terms and the images. This thesis addresses the approaches of our team MUVIS for Microsoft research Bing image retrieval challenge to measure the relevance of web images and the query given in text form. This challenge is to develop an image-query pair scoring system to assess the effectiveness of query terms in describing the images. The provided dataset included a training set containing more than 23 million clicked image-query pairs collected from the web (One year). Also, a development set was collected which had been manually labelled. On each image-query pair, a floating-point score was produced. The floating-point score reflected the relevancy of the query to describe the given image, with higher number including higher relevance and vice versa. Sorting its corresponding score for all its associated images produced the retrieval ranking for the images of any query. The system developed by MUVIS team consisted of five modules. Two main modules were text processing module and principal component analysis assisted perceptron regression with random sub-space selection. To enhance evaluation accuracy, three complementary modules i.e. face bank, duplicate image detector and optical character recognition were also developed. Both main module and complementary modules relied on results returned by text processing module. OverFeat features extracted over text processing module results acted as input for principal component analysis assisted perceptron regression with random sub-space selection module which further transformed the features vector. The relevance score for each query-image pair was achieved by comparing the feature of the query image and the relevant training images. For features extraction, used in the face bank and duplicate image detector modules, we used CMUVIS framework. CMUVIS framework is a distributed computing framework for big data developed by the MUVIS group. Three runs were submitted for evaluation: “Master”, “Sub2”, and “Sub3”. The cumulative similarity was returned as the requested images relevance. Using the proposed approach we reached the value of 0.5099 in terms of discounted cumulative gain on the development set. On the test set we gained 0.5116. Our solution achieved fourth place in Microsoft Research Bing grand challenge 2014 for master submission and second place for overall submission

An Optimal Region Of Interest Localization Using Edge Refinement Filter And Entropy-Based Measurement For Point Spread Function Stimation

The use of edges to determine an optimal region of interest (ROI) location is increasingly becoming popular for image deblurring. Recent studies have shown that regions with strong edges tend to produce better deblurring results. In this study, a direct method for ROI localization based on edge refinement filter and entropy-based measurement is proposed. Using this method, the randomness of grey level distribution is quantitatively measured, from which the ROI is determined. This method has low computation cost since it contains no matrix operations. The proposed method has been tested using three sets of test images - Dataset I, II and III. Empirical results suggest that the improved edge refinement filter is competitive when compared to the established edge detection schemes and achieves better performance in the Pratt's figure-of-merit (PFoM) and the twofold consensus ground truth (TCGT); averaging at 15.7 % and 28.7 %, respectively. The novelty of the proposed approach lies in the use of this improved filtering strategy for accurate estimation of point spread function (PSF), and hence, a more precise image restoration. As a result, the proposed solutions compare favourably against existing techniques with the peak signal-to-noise ratio (PSNR), kernel similarity (KS) index, and error ratio (ER) averaging at 24.8 dB, 0.6 and 1.4, respectively. Additional experiments involving real blurred images demonstrated the competitiveness of the proposed approach in performing restoration in the absent of PSF