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Multiple Kernel Visual-Auditory Representation Learning for Retrieval
Hong Zhang1,2 & Wenping Zhang1 & Wenhe Liu3 & Xin Xu1 & Hehe Fan4
Received: 4 October 2015 /Revised: 3 December 2015 /Accepted: 21 January 2016 / Published online: 23 February 2016 # Springer Science+Business Media New York 2016
Abstract Cross-media data representation, which focuses on semantics understanding of multi- media data in different modalities, is a rising hot topic in web media data analysis. The most challenging issues for cross-media data representation include: how to find underlying content-level data correlations and how to use such correlations in the representation model.
Most traditional web media data analysis works are based on single modality data sources, such as Flickr images or YouTube videos, leaving cross-media data representation and semantics understanding wide open. In this paper, we propose a multiple kernel visual-auditory representation learning approach, which learns cross-media correlations from visual and auditory feature spaces with multiple kernel strategies.
Besides, we give cross-media distance measure for image-audio retrieval in the mutual subspace of co-occurrence. Experiment results on the collected image-audio database are encour- aging, and show that the performance of our approach is effective from multiple perspectives.
Keywords Multiplekernellearning.Visual-auditorydatarepresentation. Cross-mediaretrieval
1 Introduction
Multimedia representation learning has drawn tremendous research attention in the past decades. In areas of Content-based Image Retrieval (CBIR) [9, 19, 31], multimedia data
Multimed Tools Appl (2016) 75:9169–9184 DOI 10.1007/s11042-016-3294-5
Hong Zhang zhanghong_wust@163.com
1 College of Computer Science & Technology, Wuhan University of Science & Technology, Wuhan 430081, China
2 Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China
3 The Centre for Quantum Computation & Intelligent Systems, the University of Technology, Sydney (UTS), Sydney, Australia
4 Baidu, Beijing, China
http://crossmark.crossref.org/dialog/?doi=10.1007/s11042-016-3294-5&domain=pdf
clustering and classification [12, 28, 38], face and motion recognition [11, 24], event detection [1–3], etc. Abundant representation learning methods have been proposed to explore a semantic level data representation model which could be used to better understand underlying data correlations. For example, in CBIR research subspace learning is frequently used to bridge the gap between low-level visual features and high-level image semantics so as to build the semantic data representation. However, most of these works have been focused on multimedia data of single modality, such as image or audio, and cross-media data represen- tation learning is mostly ignored [32, 35]. It is interesting and challenging to retrieval multimedia data of different modalities at the same time, especially nowadays different kinds of multimedia data usually coexist in web sources representing similar semantics.
Content feature is the carrier of multimedia semantics. The main challenging problem for cross-media data representation lies in two aspects: how to find underlying feature correlations among multimedia data of different modalities, and how to use such correlations in cross- media data representation. Considering these two issues, some researchers have proposed representation learning models under certain cross-media data environments. For example, Yi Yang et al. [33] proposed a multi-feature fusion algorithm based on Hierarchical Regression to learn general multimodal semantics, and it was verified with the multimodal document database, which contained text, image and audio. Paper [36] proposed a cross-media repre- sentation learning framework, which explored inherent feature correlations and discovered external useful knowledge based on nonlinear low-level feature analysis. Paper [40] learned the uniform cross-media correlation graph, in which different kinds of multimedia objects are represented exactly in the same way. Most of these works explored underlying cross-media correlation and built multimodal data representation with the help of prior knowledge, such as page links, user comments and tagging. However, underlying cross-media correlation among heterogeneous low-level content features is mostly ignored or underestimated. Experimental evidence has shown that different kinds of multimedia data carry their contribution to high- level semantics so that the presence of one modality has usually a Bcomplementary effect^ with the other [33]. Our previous work on cross-media data analysis also showed that such complementary information can be explored and utilized to improve multimedia semantics understanding results [37, 39].
However, it is difficult to learn effective cross-media content representation because multimedia data of different modalities originally reside in heterogeneous low-level feature spaces. Although image and audio data may represent similar semantics, such as an image of bird and an audio clip of bird singing, it is challenging to find a unified representation for both bird images and audio clips. In this paper, we propose a Multiple Kernel Visual-Auditory Representation Learning (MKVARL) method for retrieval. Our framework is formulated based on two typical modalities, i.e., image and audio. In preprocessing, considering audio is a kind of time series data while image data is static, we use fuzzy clustering method proposed in our previous work to get audio indexes so that all audio data is represented in the same dimension. Then, inspired by the recent multiple kernel learning algorithm in visual search [27], we propose multiple kernel visual-auditory learning. Specifically, we first map low-level image feature matrix and audio feature matrix into high-dimensional kernel spaces with multiple kernel functions in order to better explore underlying cross-media correlations; secondly, we calculate visual-auditory canonical correlations between a pair of kernel spaces, and maximize such correlation when we map kernel spaces into the low-dimensional Isomorphic Visual-Auditory Sub- space (IVA-Subspace). With multiple kernel learning, cross-media data correlations are
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analyzed from different aspects, and more useful information could be explored in the high-dimensional kernel spaces instead of original visual feature space and auditory feature space. Furthermore, we discuss how to apply our MKVARL method into cross- media retrieval between image and audio. Experiments and comparisons verify the validity, superiority and applicability of our approach from different aspects.
The rest of this paper is organized as follows. Section 2 discusses related works from two aspects. Section 3 presents multiple kernel visual-auditory representation learning based on image and audio samples, and describes how to enable flexible cross-media retrieval between image and audio datasets. Section 4 presents the experimental results and comparisons. We give concluding remarks in section 5.
2 Related works
As previously discussed, visual-auditory search belongs to the area of cross-media re- trieval, and our paper mainly focuses on the challenge of multi-kernel visual-auditory representation learning. Therefore, in this section, we discuss related works from the perspective of cross-media retrieval [32, 33, 35, 36] and multiple kernel distance metric learning [10, 34].
2.1 Cross-media retrieval
Cross-media retrieval originates from content-based multimedia analysis and retrieval, which is a long-standing research topic in computer vision [30]. As previously discussed, most content-based multimedia retrieval works focus on multimedia data of single modality to bridge the semantic gap between low-level features and high-level semantics [15, 29], such as Content-based Image Retrieval (CBIR) [9, 31]. Considering the content gap between different multimedia data, cross-media retrieval aims to build a flexible retrieval framework, in which users can search multimedia data with a query example of different modality [32, 35]. For example, in a cross-media retrieval system, we can obtain relevant image and audio results by submitting an image query example or an audio query example. The main challenging problem for cross-media retrieval is how to measure the similarity between different kinds of low-level feature spaces. For example, although image and audio data could represent similar semantics, it is very difficult to measure the low-level feature similarity between visual features of images and auditory features of audio clips.
In the past few years, researchers have proposed some cross-media retrieval algorithms, and provide possible solution to bridge the content gap for flexible retrieval. Most of those researches could be grouped into three categories: context-based cross-media retrieval, cross-modal video data analysis and retrieval, content-based cross-media retrieval. In the first group, context correlations, such as web links, conclusion relation and text comments, are explored and used to estimate cross-media similarity between multimedia data of different modalities. For example, Yang et al. proposed a distance measure between heterogeneous Multimedia Documents (MMD) which consisted of text, image or audio samples, and constructed a MMD semantic subspace for cross-media retrieval [34]. MMD is a typical cross-media data environment with rich context correlations. If an image and an audio clip are included in the same MMD, we can assume these two multimedia objects represent similar semantics. Web pages and PPT documents are examples of MMD.
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Secondly, video data contains different tracks of information, including key frame images, sounds and voices, text subtitles, etc. It was frequently used to synthetically analyze different tracks of low-level video features, such as visual features of key frames, auditory features of speakers and caption features. A great deal of researcher are dedicated to cross- modal retrieval between different tracks of video data [10, 26]. For example, paper [13] proposed a subject model which learned probabilistic collections between semantic con- cepts (keywords) of high frequency and multimedia objects so that users could retrieval news of different types.
Besides, a few researchers focus on how to analyze content-level statistical cross-media correlation with labeled and unlabeled data [36, 37, 39]. Although multimedia data of different modalities may Blook^ different in visual and auditory representations, they may have statistical content-level correlation which could be explored and used for retrieval. For example, paper [36] proposed the isomorphic cross-media subspace mapping algo- rithm, which calculated and maintained underlying canonical correlation between visual feature matrix of images and auditory feature matrix of audio clips during subspace mapping.
2.2 Multiple kernel distance metric learning
Kernel methods typically consist of two part. The first part maps the input feature space into another space which is often much higher or even infinite dimensionality by applying a nonlinear function; the second part usually applies a linear method in the high dimensional space. Kernel-based methods are not new for multimedia retrieval, for example, kernel SVM algorithms have been successfully introduced into the CBIR tasks [20]. In kernel-based multimedia representation and distance metric learning literature, some algorithms were proposed for similarity learning in CBIR. Connections between representation learning and kernel learning, which can provide kernelization for a set of metric learning methods, have been revealed in recent studies [6].
Multiple kernel learning (MKL) [8, 16] now is a hot research topic in machine learning. It has been used in various studies and applications with great success, such as bioinfor- matics, computer vision, and natural language processing. Paper [8] found the optimal combination of multiple kernels for learning classifiers towards a given classification task. In addition, several recent studies address multiple kernel learning for multi-class and multi-labeled data so as to improve system efficiency and generality [7, 22, 23]. Compared to a single kernel, such as SVM, MKL attempts to achieve better results by combining several base kernels instead of using only one specific kernel [21]. MKL allows the practitioner to optimize over linear combinations of kernels, and it has focused on both formulation learning as well as the corresponding optimization. Different applications need different formulations, the existing MKL methods use different learning functions for determining the kernel combinations [5].
In terms of combination functions, most MKL studies often work with linear combinations which have two basic categories: unweighted sum and weighted sum. In the unweighted sum case, we use sum or mean of the kernels as the combined kernel; in the weighted case, we can linearly optimize weight for each kernel. Besides, there are nonlinear combination studies which apply nonlinear functions of kernel (e.g., multiplication, power and exponentiation). Besides, as for different target functions, MKL algorithms are typically categorized into three groups: the similarity-based
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functions; the structural risk functions and the Bayesian functions. All MKL algorithms have the same goal of learning the optimum combination of multiple kernels, but the differences between our methods with others lie in that we aim to learn a kernel-based similarity function for image retrieval while conventional MKL studies often handle classification tasks.
2.3 Discussion
Above related works obtained satisfying results on multimedia representation and retriev- al. Our approach of multiple kernel visual-auditory representation and retrieval differs from most related works in the following aspects: we aim to learn a kernel-based similarity function for visual-auditory retrieval while conventional MKL studies often handle single- modality multimedia data analysis tasks. On the other hand, content-based multimedia analysis and retrieval works mostly focus on single modality data and ignore the issue of cross-media correlation analysis and semantics understanding which is addressed in this paper.
3 Multiple kernel visual-auditory representation learning
We aim to learn the general visual-auditory representation framework where different types of multimedia data are represented in the isomorphic subspace and cross-media correlation could be easily measured for query results ranking. Figure 1 illustrates the flowchart of the proposed Multiple Kernel Visual-Auditory Representation Learning (MKVARL) method. The main idea of our approach is that: first, we map the audio feature matrix and the image feature matrix into k Hilbert spaces respectively; then, we analyze canonical correlations between a pair of audio Hilbert space and image Hilbert space; thirdly, we map both image samples and audio samples from Hilbert spaces into the Isomorphic Visual-Auditory Subspace (IVA-Subspace) where original canonical correlations are maximally remained. In the IVA-Subspace, we propose cross-media distance metric measure to estimate visual-auditory correlation for retrieval. In this way we can find most similar image samples or audio samples to users based on the query example users submitted.
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Introduction
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Average Score 50-85% |
40-38 points More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided. |
83-76 points Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration. |
52-49 points Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met. |
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75-1 points Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration. |
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