Content-Based Image Retrieval based on efficient fuzzy color signature

A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. To address these concerns, this paper presents a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. Such computing methodology is fairly desirable for image retrieval over large image databases.

Background subtraction is a very popular approach for vehicle detection in traffic surveillance systems. A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. However, structured motion patterns of the background (e.g., Vehicular traffic videos, waving leaves, spouting fountain, rippling water, etc.), which are distinctive from variations due to noise, are hardly tolerated in this assumption. To address these concerns, we introduce a background subtraction algorithm for temporally dynamic texture scenes. Specifically, we propose to adopt a clustering-based feature, called fuzzy color histogram (FCH), which has an ability of greatly attenuating color variations generated by background motions while still highlighting moving objects. Experimental results demonstrate that the proposed method is effective for background subtraction in dynamic texture scenes using LFCH features with adaptive updating procedure compared to several competitive methods proposed in the literature.

Effect of multi filters in glucoma detection using random forest classifier

Content-Based Image Retrieval (CBIR) systems play a crucial role in managing the exponential growth of digital image collections. Their task is to retrieve relevant images by analysing the content of a query image rather than relying on metadata. The idea here is to extract relevant features from the query image and find a similar set of features in other images. The current generation of CBIR systems faces difficulties in bridging the “semantic gap” between image features at low levels and human perception at high levels which reduces retrieval precision. The research targets CBIR performance enhancement by developing state-of-the-art deep learning techniques for feature extraction. We implemented two approaches which included a custom Convolutional Neural Network (CNN) with six convolutional layers and a finetuned ResNet50 model trained on the CIFAR-10 dataset, and the finetuned model demonstrated better performance than numerous existing CBIR systems. The networks extracted 512-dimensional feature vectors from their penultimate layer for subsequent similarity-matching operations. The fine-tuned ResNet50 model delivered better retrieval results than our custom CNN model since it reached a Mean Average Precision (MAP) score of 98.7%, while the custom CNN achieved 87.3%. We built a web-based interface that allows users to perform real-time image retrieval through multiple evaluation metrics.

As the use of digital media increases, effective retrieval and management techniques become more important. In order to extract useful information from this huge amount of data, many content based image retrieval (CBIR) systems have been developed. A typical CBIR system captures image features such as color, texture, or shape of objects in the query image and tries to retrieve images from the database with similar features. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, a CBIR system based on color histogram has been presented and a new approach for image storage and retrieval called association-based image retrieval (ABIR) has been studied. The results of both systems have been shown.

Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.

Retrieval of images based on visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. Various techniques have been implemented using these features like fuzzy color histogram, Tammura texture etc. In this paper we propose a novel method with highly accurate and retrieval efficient approach which will work on large image database with varied contents and background.

Color histogram is widely used for image indexing in content-based image retrieval (CBIR). A color histogram describes the global color distribution of an image. It is very easy to compute and is insensitive to small changes in viewing positions. However, the histogram is not robust to large appearance changes. Moreover, the histogram might give similar results for different kinds of images if the distributions of colors are same in the images. On the other hand, color correlogram is efficiently used for image indexing in content-based image retrieval. Color correlogram extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The characteristic of the color Correlogram to take into account the spatial information as well as the distribution of color pixels greatly attracts the researcher for content based image retrieval. In this paper, we propose the image bin (histogram value divisions) separation technique followed by extracting maxima of frequencies and plotting a correlogram. At first, the histogram is first calculated for an image. After that, it is subdivided into four equal bins. Each bin is subdivided into four more bins and for every such subdivision the maxima of frequencies s calculated. This information is stored in the form of a correlogram. The distance between correlogram of the query image with the corresponding correlogram of database images is calculated. The proposed algorithm is tested on a database comprising a large number of images.

In this paper we describe an approach for defining customized color descriptors for image retrieval. In particular, a customized fuzzy dominant color descriptor is proposed on the basis of a finite collection of fuzzy colors designed specifically for a certain user. Fuzzy colors modeling the semantics of a color name are defined as fuzzy subsets of colors on an ordinary color space, filling the semantic gap between the color representation in computers and the subjective human perception. The design of fuzzy colors is based on a collection of color names and corresponding crisp representatives provided by the user. The descriptor is defined as a fuzzy set over the customized fuzzy colors i.e. a level-2 fuzzy set, taking into account the imprecise concept that is modelled, in which membership degrees represent the dominance of each color. The dominance of each fuzzy color is calculated on the basis of a fuzzy quantifier representing the notion of dominance, and a fuzzy histogram representing as a fuzzy quantity the percentage of pixels that match each fuzzy color. The obtained descriptor can be employed in a large amount of applications. We illustrate the usefulness of the descriptor by a particular application in image retrieval.

The timely identification of dermatological diseases together with their precise diagnosis enables proper medical interventions that minimize further disease complications. The authors conduct a performance- based evaluation of AdaBoost classifier with handcrafted image features for automated skin disease classification systems. The researchers worked with a specific collection of 38,000 dermatological pictures which included ten different disease types from Melanoma to Eczema to Psoriasis to Fungal Infections. The research investigated six image feature extraction methods including Gabor Filter and JPEG Coefficient Filter (JPEGCF) and Pyramid Histogram of Oriented Gradients Filter (PHOGF) as well as Simple Color Histogram Filter (SCHF) and Fuzzy Color and Texture Histogram Filter (FCTH) with Fuzzy Opponent Histogram Filter (FOHF). The research used feature vectors extracted from AdaBoost classifiers in WEKA within 10-fold cross-validation procedures for training purposes. The evaluation metrics consisted of accuracy, precision, recall, ROC, PRC as well as training time. Within the examined models Gabor+AdaBoost demonstrated maximum rates of accuracy (97.78%) alongside precision (0.98) and recall (0.98) but it required the most execution time at 14.4s. When weighed against each other JPEGCF+AdaBoost maintained equivalent accuracy at 97.59% but required only 0.21 seconds to complete tasks thus earning status as a balanced choice for this system. SCHF+AdaBoost proved suitable for real-time operations by delivering 94.11% accuracy results within 0.06 seconds computing time. The study reveals that Gabor descriptors provide optimal predictive accuracy but JPEGCF along with SCHF demonstrate the best combination of performance with computational speed.

For the image classification task, the color histogram is widely used as an important color feature indicating the content of the image. However, the high-resolution color histograms are usually of high dimension and contain much redundant information which does not relate to the image content, while the low-resolution histograms cannot provide adequate discriminative information for image classification. In this paper, a new color feature representation is proposed which not only takes the correlation among neighbouring components of the conventional color histogram into account but removes the redundant information as well. A high-resolution, uniform quantized color histogram is first obtained from the image. Then the redundant bins are removed and some neighbouring bins are combined together to generate a new feature component to maximize the discriminative ability. The mutual information is adopted to evaluate the discriminative power of a specific feature set and an iterative algorithm is performed to derive the histogram quantization and their corresponding feature generation. To illustrate the effectiveness of the proposed feature representation, an application of detecting adult images, i.e., image classification between erotic and benign images, is carried out. Two widely used classification techniques, SVM and Adaboost, are employed as the classifier. Experimental results show the superior performance of our color representation compared with the conventional color histogram in image classification.

Early methods of image indexing relied heavily on color histograms, which characterize the global content of images.However, global indexing methods proved to be unsatisfactory, and researchers now employ more localized measures ofimage content, based on relatively small regions. At the same time, it has also become clear that image indexing shouldbe based on higher-level visual content. This raises an important question: Can the higher-level content of images bereliably indexed using local analysis?Ž In general, humans are better at indexing mid-level and high-level visual contentthan todays automated indexing algorithms. Therefore, it makes sense to ascertain how well humans can perform mid-level or high-level indexing, based on small regions. This paper describes research that employs a set of outdoor sceneryimages (called the NaturePix image set) to compare how successfully humans can label the visual content of smallregions of natural images when (1) these regions are seen in the context of the larger image, and (2) when these regionsare extracted from (and are seen in isolation from) that larger image. The results of these experiments indicate whattypes of higher-level image content can be recognized locally, and how successfully high-level image content can beindexed on the basis of local feature analysis.Keywords: Content based image retrieval, Image indexing, Semantic indexing, Lexical basis functions, Image content,Visual content, Semantic content, NaturePix, Local content analysis, Feature detectors

Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database.Image contents are plays significant role for image retrieval.There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design.Each image has three contents such as: color, texture and shape features.Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results.Color histogram and texture features have potential to retrieve similar images on the basis of their properties.As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query.To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solution.

Content-based image retrieval (CBIR) has suffered from the lack of linkage between low-level features and high-level semantics. Although relevance feedback (RF) CBIR provides a promising solution involving human interaction, certain query images poorly represented by low-level features still have unsatisfactory retrieval results. An innovative method has been proposed to increase the percentage of relevance of target image database by using graph cuts theory with the maximum-flow/minimum-cut algorithm and relevance feedback. As a result, the database is reformed by keeping relevant images while discarding irrelevant images. The relevance is increased and thus during following RF-CBIR process, previously poorly represented relevant images have higher probability to appear for selection. Better performance and retrieval results can thus be achieved.

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.