Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

A Comparative Study on Feature Extraction using Texture and Shape for Content Based Image Retrieval

Abstract: Image Retrieval is the basic requirement of today’s life in present scenario. Because of huge amount of different types of images are added in database from different sources for retrieval of the image, different kinds of processing is required to extract the relevant features from them. In this paper, comparisons of combination texture and shape features are done with texture Gray Level Co-occurrence Matrix and Hu-moments and the combination of tamura texture and shape invariant Hu-moments. For the performance evaluation of the system we use most commonly used methods namely precision and recall.

Integrating wavelets with clustering and indexing for effective content-based image retrieval

Abstract: Recent development in technology influenced our daily life and the way people communicate and store data. There is a clear shift from traditional methods to sophisticated techniques; this maximizes the utilization of the widely available digital media. People are able to take photos using hand held devices and there is a massive increase in the volume of photos digitally stored. Digital devices are also shaping the medical field. Scanners are available for every part of the body to help identifying problems. However, this tremendous increase in the number of digitally captured and stored images necessitates the development of advanced techniques capable of classifying and effectively retrieving relevant images when needed. Thus, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images with minimum human intervention. The research community is competing for more efficient and effective methods as CBIR systems may be heavily employed in serving time critical monitoring applications in homeland security, scientific and medical domains, among others. All of this motivated for the work described in this paper. We propose a novel approach which uses a well-known clustering algorithm k-means and a database indexing structure B+-tree to facilitate retrieving relevant images in an efficient and effective way. Cluster validity analysis indexes combined with majority voting are employed to verify the appropriate number of clusters. While searching for similar images, we consider images from the closest cluster and from other nearby clusters. We introduced two new parameters named c"G and c"S to determine the distance range to be searched in each cluster. These parameters enable us to find similar images even if the query image is misclustered and to further narrow down the search space for large clusters. To determine values of c"G and c"S, we introduced a new formula for gain measurement and we iteratively find the best gain value and accordingly set the values. We used Daubechies wavelet transformation for extracting the feature vectors of images. The reported test results are promising. The results demonstrate how using data mining techniques could improve the efficiency of the CBIR task without sacrificing much from the accuracy of the overall process.

Learning to rank for content-based image retrieval

Abstract: In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results The typical ranking strategy used by many CBIR systems is to employ image content descriptors, so that returned images that are most similar to the query image are placed higher in the rank While this strategy is well accepted and widely used, improved results may be obtained by combining multiple image descriptors In this paper we explore this idea, and introduce algorithms that learn to combine information coming from different descriptors The proposed learning to rank algorithms are based on three diverse learning techniques: Support Vector Machines (CBIR-SVM), Genetic Programming (CBIR-GP), and Association Rules (CBIR-AR) Eighteen image content descriptors(color, texture, and shape information) are used as input and provided as training to the learning algorithms We performed a systematic evaluation involving two complex and heterogeneous image databases (Corel e Caltech) and two evaluation measures (Precision and MAP) The empirical results show that all learning algorithms provide significant gains when compared to the typical ranking strategy in which descriptors are used in isolation We concluded that, in general, CBIR-AR and CBIR-GP outperforms CBIR-SVM A fine-grained analysis revealed the lack of correlation between the results provided by CBIR-AR and the results provided by the other two algorithms, which indicates the opportunity of an advantageous hybrid approach

Attention-based Pyramid Aggregation Network for Visual Place Recognition

Abstract: Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.