The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.

Recent Advance in Content-based Image Retrieval: A Literature Survey

Content-based image retrieval (CBIR) is a mechanism that is used to retrieve similar images from an image collection. In this paper, an effective novel technique is introduced to improve the performance of CBIR on the basis of visual words fusion of scale-invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. SIFT performs better on scale changes and on invariant rotations. However, SIFT does not perform better in the case of low contrast and illumination changes within an image, while LIOP performs better in such circumstances. SIFT performs better even at large rotation and scale changes, while LIOP does not perform well in such circumstances. Moreover, SIFT features are invariant to slight distortion as compared to LIOP. The proposed technique is based on the visual words fusion of SIFT and LIOP descriptors which overcomes the aforementioned issues and significantly improves the performance of CBIR. The experimental results of the proposed technique are compared with another proposed novel features fusion technique based on SIFT-LIOP descriptors as well as with the state-of-the-art CBIR techniques. The qualitative and quantitative analysis carried out on three image collections, namely, Corel-A, Corel-B, and Caltech-256, demonstrate the robustness of the proposed technique based on visual words fusion as compared to features fusion and the state-of-the-art CBIR techniques.

/pdf/a-novel-technique-based-on-visual-words-fusion-analysis-of-z697gmlhir.pdf

A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users’ intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

Discriminative Multi-View Interactive Image Re-Ranking

Large amount of multi-media content, generated by various image capturing devices, is shared and downloaded by millions of users across the globe, every second. High computation cost is inured in providing visually similar results to the user’s query. Annotation based image retrieval is not efficient since annotations vary in terms of languages while pixel wise matching of images is not preferred since the orientation, scale, image capturing style, angle, storage pattern etc. bring huge amount of variations in the images. Content Based Image Retrieval (CBIR) system is frequently used in such cases since it computes similarity between query image and images of reference dataset efficiently. A Bi-layer Content Based Image Retrieval (BiCBIR) system has been proposed in this paper which consists of two modules: first module extracts the features of dataset images in terms of color, texture and shape. Second module consists of two layers: initially all images are compared with query image for shape and texture feature space and indexes of M most similar images to the query image are retrieved. Next, M images retrieved from previous layer are matched with query image for shape and color feature space and F images similar to the query image are returned as a output. Experimental results show that BiCBIR system outperforms the available state-of-the-art image retrieval systems.

Xiang-Yang Wang

Papers

An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification