Binary Wavelet Transform Based Histogram Feature for Content Based Image Retrieval

In this paper a new visual feature, binary wavelet transform based histogram (BWTH) is proposed for content based image retrieval. BWTH is facilitated with the color as well as texture properties. BWTH exhibits the advantages of binary wavelet transform and histogram. The performance of CBIR system with proposed feature is observed on Corel 1000 (DB1) and Corel 2450 (DB2) natural image database in color as well as gray space. The results analysis of DB1 database illustrates the better average precision and average recall of proposed method in RGB space (73.82%, 44.29%) compared to color histogram (70.85%, 42.16%), auto correlogram (66.15%, 39.52%) and discrete wavelet transform (60.83%, 38.25%). In case of gray space also performance of proposed method (66.69%, 40.77%) is better compared to auto correlogram (57.20%, 35.31%), discrete wavelet transform (52.70%, 32.98%) and wavelet correlogram (64.3%, 38.0%). It is verified that in case of DB2 database also average precision, average recall and average retrieval rate of proposed method are significantly better

Improving performance of content based image retrieval system with color features

Content based image retrieval (CBIR) encompasses a variety of techniques with a goal to solve the problem of searching for digital images in a large database by their visual content. Applications where the retrieval of similar images plays a crucial role include personal photo and art collections, medical imaging, multimedia publications and video surveillance. Main objective of our study was to try to improve the performance of the query-by-example image retrieval system based on texture features – Gabor wavelet and wavelet transform – by augmenting it with color information about the images, in particular color histogram, color autocorrelogram and color moments. Wang image database comprising 1000 natural color images grouped into 10 categories with 100 images was used for testing individual algorithms. Each image in the database served as a query image and the retrieval performance was evaluated by means of the precision and recall. e number of retrieved images ranged from 10 to 80. e best CBIR performance was obtained when implementing a combination of all 190 texture- and color features. Only slightly worse were the average precision and recall for the texture- and color histogram-based system. is result was somewhat surprising, since color histogram features provide no color spatial informa- tion. We observed a 23% increase in average precision when comparing the system containing a combination of texture- and all color features with the one consisting of exclusively texture descriptors when using Euclidean distance measure and 20 retrieved images. Addition of the color autocorrelogram features to the texture de- scriptors had virtually no e ect on the performance, while only minor improvement was detected when adding rst two color moments – the mean and the standard deviation. Similar to what was found in the previous studies with the same image database, average precision was very high in case of dinosaurs and owers and very low with beach, food, monuments and mountains images

Naive bayes multi-label classification approach for high-voltage condition monitoring

This paper addresses for the first time the multilabel classification of High-Voltage (HV) discharges captured using the Electromagnetic Interference (EMI) method for HV machines. The approach involves feature extraction from EMI time signals, emitted during the discharge events, by means of 1D-Local Binary Pattern (LBP) and 1D-Histogram of Oriented Gradients (HOG) techniques. Their combination provides a feature vector that is implemented in a naive Bayes classifier designed to identify the labels of two or more discharge sources contained within a single signal. The performance of this novel approach is measured using various metrics including average precision, accuracy, specificity, hamming loss etc. Results demonstrate a successful performance that is in line with similar application to other fields such as biology and image processing. This first attempt of multi-label classification of EMI discharge sources opens a new research topic in HV condition monitoring

Deep metric learning to rank

We propose a novel deep metric learning method by revisiting the learning to rank approach. Our method, named FastAP, optimizes the rank-based Average Precision measure, using an approximation derived from distance quantization. FastAP has a low complexity compared to existing methods, and is tailored for stochastic gradient descent. To fully exploit the benefits of the ranking formulation, we also propose a new minibatch sampling scheme, as well as a simple heuristic to enable large-batch training. On three few-shot image retrieval datasets, FastAP consistently outperforms competing methods, which often involve complex optimization heuristics or costly model ensembles.Accepted manuscrip

A hybrid content based image retrieval system using log-gabor filter banks

In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall