Reading Proposal

As discussed with Djoerd I am going backwards throught the years of SIGIR-Proceedings and see what could be relevant for me:

Cao, G., Nie, J., and Bai, J. 2005. Integrating word relationships into language models. In Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Salvador, Brazil, August 15 - 19, 2005). SIGIR '05. ACM Press, New York, NY, 298-305. DOI= http://doi.acm.org/10.1145/1076034.1076086
Why? Because I need to know about language models!?!

In this paper, we propose a novel dependency language modeling approach for information retrieval. The approach extends the existing language modeling approach by relaxing the independence assumption. Our goal is to build a language model in which various word relationships can be integrated. In this work, we integrate two types of relationship extracted from WordNet and co-occurrence relationships respectively. The integrated model has been tested on several TREC collections. The results show that our model achieves substantial and significant improvements with respect to the models without these relationships. These results clearly show the benefit of integrating word relationships into language models for IR.

Cai, D. and He, X. 2005. Orthogonal locality preserving indexing. In Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Salvador, Brazil, August 15 - 19, 2005). SIGIR '05. ACM Press, New York, NY, 3-10. DOI= http://doi.acm.org/10.1145/1076034.1076039
Why: Indexing methods are of relevance?

We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is extremely sensitive to the number of dimensions. This makes it difficult to estimate the intrinsic dimensionality, while inaccurately estimated dimensionality would drastically degrade its performance. One reason leading to this problem is that LPI is non-orthogonal. Non-orthogonality distorts the metric structure of the document space. In this paper, we propose a new algorithm called Orthogonal LPI. Orthogonal LPI iteratively computes the mutually orthogonal basis functions which respect the local geometrical structure. Moreover, our empirical study shows that OLPI can have more locality preserving power than LPI. We compare the new algorithm to LSI and LPI. Extensive experimental results show that Orthogonal LPI obtains better performance than both LSI and LPI. More crucially, it is insensitive to the number of dimensions, which makes it an efficient data preprocessing method for text clustering, classification, retrieval, etc.

Aslam, J. A., Yilmaz, E., and Pavlu, V. 2005. The maximum entropy method for analyzing retrieval measures. In Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Salvador, Brazil, August 15 - 19, 2005). SIGIR '05. ACM Press, New York, NY, 27-34. DOI= http://doi.acm.org/10.1145/1076034.1076042
Why: Evaluation methods are important (know your enemy!).

We present a model, based on the maximum entropy method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs. Our methodology treats the value of such a measure as a constraint on the distribution of relevant documents in an unknown list, and the maximum entropy distribution can be determined subject to these constraints. For good measures of overall performance (such as average precision), the resulting maximum entropy distributions are highly correlated with actual distributions of relevant documents in lists as demonstrated through TREC data; for poor measures of overall performance, the correlation is weaker. As such, the maximum entropy method can be used to quantify the overall quality of a retrieval measure. Furthermore, for good measures of overall performance (such as average precision), we show that the corresponding maximum entropy distributions can be used to accurately infer precision-recall curves and the values of other measures of performance, and we demonstrate that the quality of these inferences far exceeds that predicted by simple retrieval measure correlation, as demonstrated through TREC data.

Xu, C., Shao, X., Maddage, N. C., and Kankanhalli, M. S. 2005. Automatic music video summarization based on audio-visual-text analysis and alignment. In Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Salvador, Brazil, August 15 - 19, 2005). SIGIR '05. ACM Press, New York, NY, 361-368. DOI= http://doi.acm.org/10.1145/1076034.1076097

Why: They classify videos....

In this paper, we propose a novel approach for automatic music video summarization based on audio-visual-text analysis and alignment. The music video is separated into the music and video tracks. For the music track, the chorus is detected based on music structure analysis. For the video track, we first segment the shots and classify the shots into close-up face shots and non-face shots, then we extract the lyrics and detect the most repeated lyrics from the shots. The music video summary is generated based on the alignment of boundaries of the detected chorus, shot class and the most repeated lyrics from the music video. The experiments on chorus detection, shot classification, and lyrics detection using 20 English music videos are described. Subjective user studies have been conducted to evaluate the quality and effectiveness of summary. The comparisons with the summaries based on our previous method and the manual method indicate that the results of summarization using the proposed method are better at meeting users' expectations.

Ghoshal, A., Ircing, P., and Khudanpur, S. 2005. Hidden Markov models for automatic annotation and content-based retrieval of images and video. In Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Salvador, Brazil, August 15 - 19, 2005). SIGIR '05. ACM Press, New York, NY, 544-551. DOI= http://doi.acm.org/10.1145/1076034.1076127

Why? They are annotating stuff which I might have later to find a infrastructure

This paper introduces a novel method for automatic annotation of images with keywords from a generic vocabulary of concepts or objects for the purpose of content-based image retrieval. An image, represented as sequence of feature-vectors characterizing low-level visual features such as color, texture or oriented-edges, is modeled as having been stochastically generated by a hidden Markov model, whose states represent concepts. The parameters of the model are estimated from a set of manually annotated (training) images. Each image in a large test collection is then automatically annotated with the a posteriori probability of concepts present in it. This annotation supports content-based search of the image-collection via keywords. Various aspects of model parameterization, parameter estimation, and image annotation are discussed. Empirical retrieval results are presented on two image-collections | COREL and key-frames from TRECVID. Comparisons are made with two other recently developed techniques on the same datasets.

Carneiro, G. and Vasconcelos, N. 2005. A database centric view of semantic image annotation and retrieval. In Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Salvador, Brazil, August 15 - 19, 2005). SIGIR '05. ACM Press, New York, NY, 559-566. DOI= http://doi.acm.org/10.1145/1076034.1076129

Why? Because of the annotation...

We introduce a new model for semantic annotation and retrieval from image databases. The new model is based on a probabilistic formulation that poses annotation and retrieval as classification problems, and produces solutions that are optimal in the minimum probability of error sense. It is also database centric, by establishing a one-to-one mapping between semantic classes and the groups of database images that share the associated semantic labels. In this work we show that, under the database centric probabilistic model, optimal annotation and retrieval can be implemented with algorithms that are conceptually simple, computationally efficient, and do not require prior semantic segmentation of training images. Due to its simplicity, the annotation and retrieval architecture is also amenable to sophisticated parameter tuning, a property that is exploited to investigate the role of feature selection in the design of optimal annotation and retrieval systems. Finally, we demonstrate the benefits of simply establishing a one-to-one mapping between keywords and the states of the semantic classification problem over the more complex, and currently popular, joint modeling of keyword and visual feature distributions. The database centric probabilistic retrieval model is compared to existing semantic labeling and retrieval methods, and shown to achieve higher accuracy than the previously best published results, at a fraction of their computational cost.

Fan, J., Gao, Y., Luo, H., and Xu, G. 2004. Automatic image annotation by using concept-sensitive salient objects for image content representation. In Proceedings of the 27th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Sheffield, United Kingdom, July 25 - 29, 2004). SIGIR '04. ACM Press, New York, NY, 361-368. DOI= http://doi.acm.org/10.1145/1008992.1009055

Why: Special technique to index stuff.

Multi-level annotation of images is a promising solution to enable more effective semantic image retrieval by using various keywords at different semantic levels. In this paper, we propose a multi-level approach to annotate the semantics of natural scenes by using both the dominant image components and the relevant semantic concepts. In contrast to the well-known image-based and region-based approaches, we use the salient objects as the dominant image components to achieve automatic image annotation at the content level. By using the salient objects for image content representation, a novel image classification technique is developed to achieve automatic image annotation at the concept level. To detect the salient objects automatically, a set of detection functions are learned from the labeled image regions by using Support Vector Machine (SVM) classifiers with an automatic scheme for searching the optimal model parameters. To generate the semantic concepts, finite mixture models are used to approximate the class distributions of the relevant salient objects. An adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously. We have also demonstrated that our algorithms are very effective to enable multi-level annotation of natural scenes in a large-scale dataset.