Abstract book (pdf) - ICPR 2010

A new method to extract dashed lines in technical document is proposed in this paper by combining force histogram and
discrete lines. The aim is to study the spatial location of couples of connected components using force histogram and to
refine the recognition by considering surrounding discrete lines. This new model is fast and it allows a good extraction of
occulted patterns in presence of noise. Efficient common methods require several thresholds to process with technical
documents. The proposed method requires only few thresholds which can be automatically set from data.
17:00-17:20, Paper TuCT4.5
Heat Flow-Thermodynamic Depth Complexity in Networks
Escolano, Francisco, Univ. of Alicante
Lozano, Miguel Angel, Univ. of Alicante
Hancock, Edwin, Univ. of York
In this paper we establish a formal link between network complexity in terms of Birkhoff-von Newmann decompositions
and heat flow complexity (in terms of quantifying the heat flowing through the network at a given inverse temperature).
Furthermore, we also define heat flow complexity in terms of thermodynamic depth, which results in a novel approach
for characterizing networks and quantify their complexity. In our experiments we characterize several protein-protein interaction
(PPI) networks and then highlight their evolutive differences.
TuCT5 Anadolu Auditorium
Image Analysis – V Regular Session
Session chair: Kasturi, Rangachar (Univ. of South Florida)
15:40-16:00, Paper TuCT5.1
Content Adaptive Hash Lookups for Near-Duplicate Image Search by Full or Partial Image Queries
Harmanci, Oztan, Anvato Inc.
Haritaoglu, Ismail, Pol. Rain Inc.
In this paper we present a scalable and high performance near-duplicate image search method. The proposed algorithm
follows the common paradigm of computing local features around repeatable scale invariant interest points. Unlike existing
methods, much shorter hashes are used (40 bits). By leveraging on the shortness of the hashes, a novel high performance
search algorithm is introduced which analyzes the reliability of each bit of a hash and performs content adaptive hash
lookups by adaptively adjusting the “range” of each hash bit based on reliability. Matched features are post-processed to
determine the final match results. We experimentally show that the algorithm can detect cropped, resized, print-scanned
and re-encoded images and pieces from images among thousands of images. The proposed algorithm can search for a
200x200 piece of image in a database of 2,250 images with size 2400x4000 in 0.020 seconds on 2.5GHz Intel Core 2.
16:00-16:20, Paper TuCT5.2
The Good, the Bad, and the Ugly: Predicting Aesthetic Image Labels
Wu, Yaowen, RWTH Aachen Univ. Fraunhofer Inst. IAIS
Bauckhage, Christian, Fraunhofer IAIS
Thurau, Christian, Fraunhofer IAIS
Automatic classification of the aesthetic content of a picture is one of the challenges in the emerging discipline of computational
aesthetics. Any suitable solution must cope with the facts that aesthetic experiences are highly subjective and
that a commonly agreed upon theory of their psychological constituents is still missing. In this paper, we present results
obtained from an empirical basis of several thousand images. We train SVM based classifiers to predict aesthetic adjectives
rather than aesthetic scores and we introduce a probabilistic post processing step that alleviates effects due to misleadingly
labeled training data. Extensive experimentation indicates that aesthetics classification is possible to a large extent. In particular,
we find that previously established low-level features are well suited to recognize beauty. Robust recognition of
unseemliness, on the other hand, appears to require more high-level analysis.
16:20-16:40, Paper TuCT5.3
Information Fusion for Combining Visual and Textual Image Retrieval
Zhou, Xin, Geneva Univ. Hospitals and Univ. of Geneva
Depeursinge, Adrien, Geneva Univ. Hospitals and Univ. of Geneva
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