Color Matching of Images by using Minkowski-Form Distance

Content-based image retrieval CBIR is an important issue in the computer vision community Color feature is one of the most important visual feature in CBIR It is very difficult to recognize object from only shape feature because without color a shape of object looks like many other different objects so there is need of other features like color Using both features color and shape we can recognize object efficiently Color histogram is widely used for image indexing in content-based image retrieval CBIR In this paper we propose color histogram for different eight colors i e Black White Red Green Blue Yellow Magenta and Cyan to increase the efficiency of proposed algorithm The distance between different histogram of the query image with the corresponding histogram of database images are calculated by using Minkowski-Form Distance Experiment results prove that the CBIR using our new measure has better performanc

CDFIR:Cummulative distribution function based image retrieval

Content Based Image Retrieval is a well-known retrieval process in the field of Image processing. CBIR is a special way of finding similar images from huge database. CBIR utilizes three rudimentary features like color, texture and shape which plays an essential role in image retrieval. The effective image retrieval process is the need and number of computations along with the rate of retrieval should be less and high respectively. Thus a simple function using cumulative distribution function is involved in the retrieval process. In this work, we propose a new method to determine similar images from large database with the use of shape feature. The Morphological processing is applied to an image to get shape feature i.e. boundary of the image. For the boundary extracted image a simple basic cumulative distribution function is applied and it results in similar intensity distribution for an image. The similarity measurement is performed using Euclidean distance. The retrieval process is compared with shape extracted feature, CDF applied feature and with edge detection algorithms. The outcome would be a less computation and good accuracy in finding the similar images. © 2017 IEEE

Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG

The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories

Image Slicing and Statistical Layer Approaches for Content-Based Image Retrieval

Two new approaches for colour features representation and comparison in digital images to handle various problems in the field of content-based image retrieval are proposed. The first approach is a double-layered system utilising a new technique, which is based on image slicing, combined with statistical features extracted and compared in each layer (ISSL). The images database is filtered in the first layer based on the similarities of brightness compared with the query image and ranked in the second layer, based on the similarities of the contrast values between the query image and the set of candidate images retrieved through the first layer. Although different distance measurements are available, the city block known as L1-norm distance measurement is used. This is due to its speed efficiency and accuracy. Different experiments are applied to different database sets, containing different number of images. The results show that the approach is scalable to the varying size of the database, robust, accurate, and fast. A comparison between the colour histogram approach and the proposed approach shows that the proposed system is more accurate and the speed of performance is much better. A new paradigm to choose the proper threshold value is proposed based on the autocorrelation of the distance vector. Moreover, an image retrieval system based on entropy as a visual discriminator is developed and compared with ISSL. The results show that the proposed ISSL approach is able to achieve better precision and reaches higher recall levels as compared with entropy approach. The second proposed technique for colour based retrieval is the Eigenvalues approach. Findings show that the interpretation of the Eigenvalues, as identity or signature for the square matrix, makes it possible to map this concept to the different bands of the image. The approach relies on calculating the accumulative distances between the query image and the images database, using the accumulative Eigenvalues of each band. The approach is tested, using different image queries over different database sets and the results are promising. Furthermore, the proposed approach is compared with ISSL approach and entropy approach, using different query images over a database set of 2000 images. In addition, a shape-based retrieval system is proposed. The system is double-layered, in which the first layer is used to filter the images database based on colour similarity. This allows the reduction in the number of candidate images, which need to be manipulated, using the shape retrieval technique in the second layer. The technique utilises a low-level image processing operations with “Dilate” as a morphological operator. Laplacian of Gaussian (LoG) is used to smoothen and detect the edges of the objects. Dilate on the other hand is used to solidify the object and fill in the holes, and correlation coefficient is proposed as a new means to shape similarity measurement. Experiments show that the approach is fast, flexible, and the retrieval of images is highly accurate. It is also able to overcome the numerous problems that are associated with the usage of the low-level image processing operation in image retrieval

On the use of edge orientation and distance for content-based image retrieval

[[abstract]]Recently, various features for content-based image retrieval (CBIR) have been proposed, such as texture, color, shape, and spatial features. In this paper we propose a new feature, called orientation-distance histogram for CBIR. Firstly, we transform the RGB color model of a given image to the HSI color model and detect edge points by using the H-vector information. Secondly, we evaluate the orientation-distance histogram from the edge points to form a feature vector. After normalization of feature, our proposed method can cope with most problems of variations in image. Finally, we show some results of query for real life images with the precision and recall rates to measure the performance. The experimental results show that the proposed retrieval method is efficient and effective[[notice]]補正完畢[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20051013~20051015[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Beijing, Chin

Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG

The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories

Content-based indexing of low resolution documents

In any multimedia presentation, the trend for attendees taking pictures of slides that interest them during the presentation using capturing devices is gaining popularity. To enhance the image usefulness, the images captured could be linked to image or video database. The database can be used for the purpose of file archiving, teaching and learning, research and knowledge management, which concern image search. However, the above-mentioned devices include cameras or mobiles phones have low resolution resulted from poor lighting and noise. Content-Based Image Retrieval (CBIR) is considered among the most interesting and promising fields as far as image search is concerned. Image search is related with finding images that are similar for the known query image found in a given image database. This thesis concerns with the methods used for the purpose of identifying documents that are captured using image capturing devices. In addition, the thesis also concerns with a technique that can be used to retrieve images from an indexed image database. Both concerns above apply digital image processing technique. To build an indexed structure for fast and high quality content-based retrieval of an image, some existing representative signatures and the key indexes used have been revised. The retrieval performance is very much relying on how the indexing is done. The retrieval approaches that are currently in existence including making use of shape, colour and texture features. Putting into consideration these features relative to individual databases, the majority of retrievals approaches have poor results on low resolution documents, consuming a lot of time and in the some cases, for the given query image, irrelevant images are obtained. The proposed identification and indexing method in the thesis uses a Visual Signature (VS). VS consists of the captures slides textual layout’s graphical information, shape’s moment and spatial distribution of colour. This approach, which is signature-based are considered for fast and efficient matching to fulfil the needs of real-time applications. The approach also has the capability to overcome the problem low resolution document such as noisy image, the environment’s varying lighting conditions and complex backgrounds. We present hierarchy indexing techniques, whose foundation are tree and clustering. K-means clustering are used for visual features like colour since their spatial distribution give a good image’s global information. Tree indexing for extracted layout and shape features are structured hierarchically and Euclidean distance is used to get similarity image for CBIR. The assessment of the proposed indexing scheme is conducted based on recall and precision, a standard CBIR retrieval performance evaluation. We develop CBIR system and conduct various retrieval experiments with the fundamental aim of comparing the accuracy during image retrieval. A new algorithm that can be used with integrated visual signatures, especially in late fusion query was introduced. The algorithm has the capability of reducing any shortcoming associated with normalisation in initial fusion technique. Slides from conferences, lectures and meetings presentation are used for comparing the proposed technique’s performances with that of the existing approaches with the help of real data. This finding of the thesis presents exciting possibilities as the CBIR systems is able to produce high quality result even for a query, which uses low resolution documents. In the future, the utilization of multimodal signatures, relevance feedback and artificial intelligence technique are recommended to be used in CBIR system to further enhance the performance

Exploiting multimedia content : a machine learning based approach

Advisors: Prof. M Gopal, Prof. Santanu Chaudhury. Date and location of PhD thesis defense: 10 September 2013, Indian Institute of Technology DelhiThis thesis explores use of machine learning for multimedia content management involving single/multiple features, modalities and concepts. We introduce shape based feature for binary patterns and apply it for recognition and retrieval application in single and multiple feature based architecture. The multiple feature based recognition and retrieval frameworks are based on the theory of multiple kernel learning (MKL). A binary pattern recognition framework is presented by combining the binary MKL classifiers using a decision directed acyclic graph. The evaluation is shown for Indian script character recognition, and MPEG7 shape symbol recognition. A word image based document indexing framework is presented using the distance based hashing (DBH) defined on learned pivot centres. We use a new multi-kernel learning scheme using a Genetic Algorithm for developing a kernel DBH based document image retrieval system. The experimental evaluation is presented on document collections of Devanagari, Bengali and English scripts. Next, methods for document retrieval using multi-modal information fusion are presented. Text/Graphics segmentation framework is presented for documents having a complex layout. We present a novel multi-modal document retrieval framework using the segmented regions. The approach is evaluated on English magazine pages. A document script identification framework is presented using decision level aggregation of page, paragraph and word level prediction. Latent Dirichlet Allocation based topic modelling with modified edit distance is introduced for the retrieval of documents having recognition inaccuracies. A multi-modal indexing framework for such documents is presented by a learning based combination of text and image based properties. Experimental results are shown on Devanagari script documents. Finally, we have investigated concept based approaches for multimedia analysis. A multi-modal document retrieval framework is presented by combining the generative and discriminative modelling for exploiting the cross-modal correlation between modalities. The combination is also explored for semantic concept recognition using multi-modal components of the same document, and different documents over a collection. An experimental evaluation of the framework is shown for semantic event detection in sport videos, and semantic labelling of components of multi-modal document images

Scale and Orientation-invariant Scene Similarity Metrics for Image Queries

In this paper we extend our previous work on shape-based queries to support queries on configurations of image objects. Here we consider spatial reasoning, especially directional and metric object relationships. Existing models for spatial reasoning tend to rely on pre-identified cardinal directions and minimal scale variations, assumption that cannot be considered as given in our image applications, where orientations and scale may vary substantially, and are often unknown. Accordingly, we have developed the method of varying baselines to identify similarities in direction and distance relations. Our method allows us to evaluate directional similarities without a priori knowledge of cardinal directions, and to compare distance relations even when query scene and database content differ in scale by unknown amounts. We use our method to evaluate similarity between a user-defined query scene and object configurations. Here we present this new method, and discuss its role within a broader image retrieval framework