Image classification and retrieval based on wavelet-SOM

In content based image retrieval (CBIR) process, every image has been represented in a compact set of local visual features i.e. color, texture, and/or shape of images. This set of local visual features is known as feature vector. In the CBIR process, feature vectors of images have been used to represent or to identify similar images in adequate way. As a result, feature vector construction has always been considered as an important issue since it must reflect proper image semantics using minimal amount of data. The proposed CBIR scheme is based on the combination of color and texture features. In this work initially, we have converted the given RGB image into HSV color image. Subsequently, we have considered H (hue), S (saturation), and V (intensity) components for extraction of visual image features. The texture features have been extracted from the V component of the image using 2D dual-tree complex wavelet transform (2D DT-CWT) where it analyzes the textural patterns in six different directions i.e. ±15\(^{\circ }\), ±45\(^{\circ }\), and ±75\(^{\circ }\). At the same time, we have computed the probability histograms of H and S components of the image respectively and subsequently those are divided into non-uniform bins based on cumulative probability for extraction of color based features. So, in this work both the color and texture features have been extracted simultaneously. Finally, the obtained features have been concatenated to attain the final feature vector and same is considered in image retrieval process. We have tested the novelty and performance of the proposed work in two Corel, two objects, and, a texture image datasets. The experimental results reveal the acceptable retrieval performances for different types of datasets.

This paper presents an intelligent texture anti-counterfeiting method based on human visual characteristics and DFT. By using a part of sign sequence of DFT coefficients as the feature vector of texture images, we extract a visual feature vector and then create a database of texture images' feature vectors. After comparing the Normalized Cross-correlation (NC) between the feature vectors, we achieve the purpose of automatic identification. The experimental results show that the proposed algorithm has strong robustness against common and geometric attacks.

Though traditional classification methods show well performance in classification tasks, most of them mainly lay emphasis on ‘classification’ rather than ‘cognition’. When a new object that has never been seen is encountered, the traditional methods falsely default the image as a certain category that has been studied, however, humans can first identify the image as new. In this paper, we present a memory model for visual images classification based on residual neural network and Bayesian decision (VICRB). First, the feature vectors of the visual images for each category are extracted with residual neural network and each feature component may be correctly copied or randomly produced. Then the processes about how the visual images represent, store and retrieve are modeled. The feature vector of test images is matched with the feature vectors of learned images and the likelihood ratio is computed according to probabilistic inference theory. Finally, the odds value in favor of an old over a new image is computed by all likelihood values. According to the odd value, the Bayesian decision rule is applied to the image classification. Experimental results on two benchmark images datasets show that the presented memory model performs well in images classification tasks.

This paper proposes a content-based authentication scheme for tampering detection and localization of binary images. The watermark is generated by the feature vector of the original binary image, and embedded back into the image by a structural method. The feature vector we used is Zernike moments. It can tell the degree of the tamper in the binary image. A distance between two feature vectors is defined. We authenticate image by the distance between the extracted watermark and the feature vector of the test image. Once the tampering is detected, we can locate the tampered areas by comparing different components of the distance. The experimental results show that the algorithm can locate the tamper effectively.

Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. In this thesis work, we present a steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image region. We have initially used steerable pyramid to extract texture features from query image and database images and store them in feature vectors. Second, to speed up retrieval and similarity computation, the database images are classified and the extracted regions are clustered according to their feature vectors using median vector algorithm. This process is performed before query matching takes place. Therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a satellite and medical image database. From the experimental results, it is evident that our system performs significantly better and faster compared with other existing systems. In our analysis, we provide a comparison between retrieval results based on features extracted from the whole image using steerable pyramid with median vector and features extracted from same image without median vector. The results demonstrate that each type of feature is effective for a particular type of images according to its semantic contents, and using a combination of them giving better retrieval results for almost all different classes of images in the dataset. Â

An image retrieval method is presented based on shape similarity measure for multimedia and imaging database systems. Inthe proposed algorithm, the spatial and spectral properties of images are combined using the Radon transform, bispectra, andprincipal components analysis. For each model image in the database, the original 2-D image data are reduced to a set of l-Dprojections via the Radon transform, and then a feature vector is calculated from the bispectra of the resultant l-D functions.The principal component analysis is applied to further reduce the dimension of the feature vector so that it can be storedalong with the original image in the database at a small cost of memory. The derived feature vector is considered as the indexor key of the corresponding image, which uniquely identifies the image independent of rotation, translation, and scaling(RTS). For image retrieval, the data feature vector is computed for a query image, and matched against the feature vectors ofall the model images in the database using the Tanimoto similarity measure. The closely matching images are brought out asthe searching results.The proposed technique has been tested on a large image database. The experimental results show that the retrieval accuracyis very high even for query images with low signal-to-noise ratio (SNR).Keywords: Radon transform, bispectra, principal component analysis, RTS invariant shape features, image retrieval,Tanimoto similarity measure, retrieval accuracy

In the CBIR- (Content Based Image Retrieval) technique requires low-level or primitive features- color, texture, or other data that can be taken from its image Extracting feature vectors of database images as well as query image can be calculated with the help of slant transform by considering DC & 3 AC coefficients obtained in each block of an image. Slant transform represents the gradual brightness changes in an image line effectively. By calculating the difference between feature vector data base and feature vector for a query by using the distance measuring techniques. The vector of the smaller distance is the closest to query image. The experiment is performed in the Corel 500 Image Database. Finally, CBIR results are evaluated by the recall, precision, and F-Score.

Recently, Content-based medical image retrieval (CBMIR) systems enable fast diagnosis via the assessment of the visual information in medical application. Most of the state-of-the-art CBMIR systems facing few issues: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering approaches. The reasons behind this are, inability to properly handle the “semantic gap” and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields a crucial demand for developing computationally efficient and highly effective retrieval system. For this purpose, the present study proposed an efficient retrieval system which has a four-fold: First, pre-processing and Feature extraction of input image using canonical correlation analysis (CCA). By this approach extracted the feature in both pixel and feature domains and examined more rigorously. Second, applied Fuzzy C means clustering of pixel intensity values as features based on the singular value decomposition. Through this can grouping, the image based on the pixel intensity value. Third, deep convolutional neural network with SVM classifier which makes implementable and requires only a compact feature vector representation of the stored database image with their class levels during retrieval. Finally evaluated the performance based on the measure of Mean Average Precision, Correct rate (CR), Error rate (ER), Accuracy. The classification results and learned features are used for the purpose of retrieving the medical images in a database. The proposed retrieval system performs better than the traditional approach in terms of measuring average value of precision, recall, f-measure and accuracy 95.9%, 94.96%, 95.37% and 95.798% respectively. The suggested approach is best suited towards retrieving the medical images for various part of the body.

Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database.

Radar remote sensing image retrieval algorithm based on improved Sobel operator

Automatic medical image annotation and retrieval

Important task in image database is to organize images into appropriate category using different features of images. Image classification is studied for many years. There are various techniques proposed to increase the accuracy of classification. In this paper a novel data mining based approach is proposed for content based image classification. Feature extraction and classification algorithms are two main steps in classification process. This paper proposes the use of orthogonal transform to generate the feature vector and to investigate effectiveness of different transforms (Cosine, Sine, and Walsh). Experimentation is carried on different sizes of feature vectors which are formed by taking fractional coefficients. Classification algorithm from different families such as Bayes (Naive Bayes and Bayes Net), Function (RBFNetwork and Simple Logistic), Lazy (IB1 and Kstar), Rule (Decision and Part) and Tree (BFTree, J48 Random Tree and Random Forest) are used for classification. Experimental results and its analysis have shown the Simple Logistic classifier with Walsh transform to be better for proposed data mining based image classification technique.

The Local Binary Pattern (LBP) operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification. The LBP-based operator extracts the textural information of an image by considering the neighboring pixel values. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it is less suitable for measuring the similarity of color images with rich color information. This work overcomes this problem by adding an additional color feature, namely Color Histogram Feature (CHF), along with the LBP-based feature in the image retrieval domain. Experimental result shows that the hybrid CHF and LBP-based feature presents a promising result and outperforms the existing methods over several image databases.

Bag of Features (BoF) has been successfully exploited in content-based image retrieval for several years. Due to its performance and popularity, several extensions have been proposed that involve feature description, dictionary building, feature encoding and post-query process, etc. This paper proposes a dynamically weighting scheme for BoF-based image retrieval based on feedback. It involves two contributions: (i) analyzing the statistical distribution characteristic of similar BoF representations and (ii) computing weights dynamically based on the feedback obtained from different initial query results. We quantitatively evaluate the proposed method on two different databases. Experiments confirm that the proposed weighting scheme has better performance than the baseline of BoF-based image retrieval systems. Meanwhile, the results demonstrate the effectiveness of the weighting scheme in terms of the precision of top-N returned images.

Content-Based Image Retrieval (CBIR) has become one of the trending areas of research in computer vision. In traditional CBIR, the features in a spatial domain, such as color, texture, shape, and point features, are extracted. It is often considered that apart from the spatial features, the features extracted from the frequency domain of the images can give further information on the features of an image. In this paper, we are proposing two novel methods for the purpose of feature extraction from the 2-dimensional Discrete Cosine Transform (DCT) of an image: DCT_256_Zigzag and DCT_256_2 × 2. These methods take into consideration the lower frequencies in order to determine the features in the frequency domain. The advantage of using the zigzag scanning is to have the maximum low frequency values having Higher Energies comparatively. These two features are combined with two of the existing spatial domain features: Local Binary Patterns (LBP) and Interchannel voting features to generate a global feature vector for an image. For a query image, its feature vector is compared with feature vectors of every other image in the database using d1-distance, and the images with least distance are considered most similar images to the query image. To evaluate the efficiency of these two methods, five standard performance measures such as Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are used. Six benchmark image datasets Corel-1000, Corel-5000, Corel-10000, VisTex, STex, and Color-Brodatz are used to corroborate the performance of these methods.KeywordsCBIRDCTFrequency domainInterchannel votingLBPTMRE