We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

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Image retrieval: Ideas, influences, and trends of the new age

Human vision system actively seeks interesting regions in images to reduce the search effort in tasks, such as object detection and recognition. Similarly, prominent actions in video sequences are more likely to attract our first sight than their surrounding neighbors. In this paper, we propose a spatiotemporal video attention detection technique for detecting the attended regions that correspond to both interesting objects and actions in video sequences. Both spatial and temporal saliency maps are constructed and further fused in a dynamic fashion to produce the overall spatiotemporal attention model. In the temporal attention model, motion contrast is computed based on the planar motions (homography) between images, which is estimated by applying RANSAC on point correspondences in the scene. To compensate the non-uniformity of spatial distribution of interest-points, spanning areas of motion segments are incorporated in the motion contrast computation. In the spatial attention model, a fast method for computing pixel-level saliency maps has been developed using color histograms of images. A hierarchical spatial attention representation is established to reveal the interesting points in images as well as the interesting regions. Finally, a dynamic fusion technique is applied to combine both the temporal and spatial saliency maps, where temporal attention is dominant over the spatial model when large motion contrast exists, and vice versa. The proposed spatiotemporal attention framework has been applied on over 20 testing video sequences, and attended regions are detected to highlight interesting objects and motions present in the sequences with very high user satisfaction rate.

https://crcv.ucf.edu/papers/spatiotemporal_attention_homo.pdf

Visual attention detection in video sequences using spatiotemporal cues

In this paper, we formulate saliency detection via absorbing Markov chain on an image graph model. We jointly consider the appearance divergence and spatial distribution of salient objects and the background. The virtual boundary nodes are chosen as the absorbing nodes in a Markov chain and the absorbed time from each transient node to boundary absorbing nodes is computed. The absorbed time of transient node measures its global similarity with all absorbing nodes, and thus salient objects can be consistently separated from the background when the absorbed time is used as a metric. Since the time from transient node to absorbing nodes relies on the weights on the path and their spatial distance, the background region on the center of image may be salient. We further exploit the equilibrium distribution in an ergodic Markov chain to reduce the absorbed time in the long-range smooth background regions. Extensive experiments on four benchmark datasets demonstrate robustness and efficiency of the proposed method against the state-of-the-art methods.

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Saliency Detection via Absorbing Markov Chain

https://webuser.hs-furtwangen.de/~heindl/ebte-2014ws-BCM%2014_Munmun%20Singhania.pdf

A review on automatic image annotation techniques

Clustering on multi-type relational data has attracted more and more attention in recent years due to its high impact on various important applications, such as Web mining, e-commerce and bioinformatics. However, the research on general multi-type relational data clustering is still limited and preliminary. The contribution of the paper is three-fold. First, we propose a general model, the collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Second, under this model, we derive a novel algorithm, the spectral relational clustering, to cluster multi-type interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects. Extensive experiments demonstrate the promise and effectiveness of the proposed algorithm. Third, we show that the existing spectral clustering algorithms can be considered as the special cases of the proposed model and algorithm. This demonstrates the good theoretic generality of the proposed model and algorithm.

/pdf/spectral-clustering-for-multi-type-relational-data-lskvy53txs.pdf

Spectral clustering for multi-type relational data

In this paper, we propose an iterative similarity propagation approach to explore the inter-relationships between Web images and their textual annotations for image retrieval. By considering Web images as one type of objects, their surrounding texts as another type, and constructing the links structure between them via webpage analysis, we can iteratively reinforce the similarities between images. The basic idea is that if two objects of the same type are both related to one object of another type, these two objects are similar; likewise, if two objects of the same type are related to two different, but similar objects of another type, then to some extent, these two objects are also similar. The goal of our method is to fully exploit the mutual reinforcement between images and their textual annotations. Our experiments based on 10,628 images crawled from the Web show that our proposed approach can significantly improve Web image retrieval performance.

/pdf/multi-model-similarity-propagation-and-its-application-for-2lzj705i6k.pdf

Multi-model similarity propagation and its application for web image retrieval

Bridging the cognitive gap in image retrieval has been an active research direction in recent years. Existing solutions typically require a large volume of training data that could be difficult to obtain in practice. In this paper, we propose a data-driven approach that uses Web images and their surrounding textual annotations as the source of training data to bridge the cognitive gap. We construct an image thesaurus that contains a set of codewords, each representing a semantically related subspace in the feature space. We also explore the use of query expansion based on the constructed image thesaurus for improving image retrieval performance.

Data-driven approach for bridging the cognitive gap in image retrieval

In this paper, we propose a Web image search result organizing method to facilitate user browsing. We formalize this problem as a salient image region pattern extraction problem. Given the images returned by Web search engine, we first segment the images into homogeneous regions and quantize the environmental regions into image codewords. The salient codeword "phrases" are then extracted and ranked based on a regression model learned from human labeled training data. According to the salient "phrases", images are assigned to different clusters, with the one nearest to the centroid as the entry for the corresponding cluster. Satisfying experimental results show the effectiveness of our proposed method.

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Grouping web image search result

Image clustering is an important research topic which contributes to a wide range of applications. Traditional image clustering approaches are based on image content features only, while content features alone can hardly describe the semantics of the images. In the context of Web, images are no longer assumed homogeneous and "flatdistributed but are richly structured. There are two kinds of reinforcements embedded in such data: 1) the reinforcement between attributes of different data types (intra-type links reinforcements); and 2) the reinforcement between object attributes and the inter-type links (inter-type links reinforcements). Unfortunately, most of the previous works addressing relational data failed to fully explore the reinforcements. In this paper, we propose a reinforcement clustering framework to tackle this problem. It reinforces images and texts' attributes via inter-type links and inversely uses these attributes to update these links. The iterative reinforcing nature of this framework promises the discovery of the semantic structure of images, which is the basis of image clustering. Experimental results show the effectiveness of our proposed framework.

Iteratively clustering web images based on link and attribute reinforcements

Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.

/pdf/exploring-statistical-correlations-for-image-retrieval-wt9ljgwerg.pdf

Xing Li

Papers

Multi-model similarity propagation and its application for web image retrieval

Data-driven approach for bridging the cognitive gap in image retrieval

Grouping web image search result

Iteratively clustering web images based on link and attribute reinforcements

Exploring statistical correlations for image retrieval