An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search

Image Quantification Learning Technique through Content based Image Retrieval

This paper proposes a Radial basis functionality incorporation in learning the quantification of images using Content based Image Retrieval (CBIR). The approach is trying to find out the effectiveness of Multi-Layer Perceptron (MLP) namely Radial Basis Function (RBF) through Content Based Image Retrieval. Extract the features of an image, the numeric values of each pixel is framed in to a definite input data set of image to that the neural networks MLP gives the accuracy of the prediction of that particular Image data set. This paper put forward us with new idea of neural networks structure efficiency in the accuracy of output data set which got increased by the adjustment of the weighted neurons through a Perceptron called Radial Basis Function promoting by applying k means clustering to form clusters which are parameterized with Gaussian function application. Finally compare the actual output with observed output promoting the weighted neurons adjustment for getting the actual accurate output. A new dimension, in work enhancement of neural networks technology with that of image processing

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

Image Retrieval Berdasarkan Fitur Warna, Bentuk, dan Tekstur

Along with the times, information retrieval is no longer just on textual data, but also the visual data. The technique was originally used is Text-Based Image Retrieval (TBIR), but the technique still has some shortcomings such as the relevance of the picture successfully retrieved, and the specific space required to store meta-data in the image. Seeing the shortage of Text-Based Image Retrieval techniques, then other techniques were developed, namely Image Retrieval based on content or commonly called Content Based Image Retrieval (CBIR). In this research, CBIR will be discussed based on color, shape and texture using a color histogram, Gabor and SIFT. This study aimed to compare the results of image retrieval with some of these techniques. The results obtained are by combining color, shape and texture features, the performance of the system can be improved

Retrieving biomedical images through content-based learning from examples using fine granularity

Session: Data Mining IITraditional content-based image retrieval methods based on learning from examples analyze and attempt to understand high-level semantics of an image as a whole. They typically apply certain case-based reasoning technique to interpret and retrieve images through measuring the semantic similarity or relatedness between example images and search candidate images. The drawback of such a traditional content-based image retrieval paradigm is that the summation of imagery contents in an image tends to lead to tremendous variation from image to image. Hence, semantically related images may only exhibit a small pocket of common elements, if at all. Such variability in image visual composition poses great challenges to content-based image retrieval methods that operate at the granularity of entire images. In this study, we explore a new content-based image retrieval algorithm that mines visual patterns of finer granularities inside a whole image to identify visual instances which can more reliably and generically represent a given search concept. We performed preliminary experiments to validate our new idea for content-based image retrieval and obtained very encouraging results.published_or_final_versio

Promising Large Scale Image Retrieval by Using Intelligent Semantic Binary Code Generation Technique

AbstractA scalable content based image retrieval system for large-scale www database is designed and implemented. Million images on internet is big challenge for accurate and efficient image retrieval as per user requirement. Proposed system exploits semantic binary code generation techniques with semantic hashing function, fine and coarse similarity measure technique, automatic and manual relevance feedback technique which improve accuracy, speed of image retrieval. With dramatic growth of internet technology, scalable image retrieval system is a need of recent web based image retrieval applications such as biomedical imaging, medical diagnosis, space science application etc. Proposed system accomplish requirement of scalable, accurate and swift image retrieval system. Experimental result clearly shows that performance of image retrieval is improved in term of accuracy, efficiency and retrieval time

Large scale evaluations of multimedia information retrieval: the TRECVid experience

Information Retrieval is a supporting technique which underpins a broad range of content-based applications including retrieval, filtering, summarisation, browsing, classification, clustering, automatic linking, and others. Multimedia information retrieval (MMIR) represents those applications when applied to multimedia information such as image, video, music, etc. In this presentation and extended abstract we are primarily concerned with MMIR as applied to information in digital video format. We begin with a brief overview of large scale evaluations of IR tasks in areas such as text, image and music, just to illustrate that this phenomenon is not just restricted to MMIR on video. The main contribution, however, is a set of pointers and a summarisation of the work done as part of TRECVid, the annual benchmarking exercise for video retrieval tasks