899 research outputs found
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval
In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low
discriminative power, so false positive matches occur prevalently. Apart from
the information loss during quantization, another cause is that the SIFT
feature only describes the local gradient distribution. To address this
problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform
feature fusion at indexing level. Basically, complementary features are coupled
into a multi-dimensional inverted index. Each dimension of c-MI corresponds to
one kind of feature, and the retrieval process votes for images similar in both
SIFT and other feature spaces. Specifically, we exploit the fusion of local
color feature into c-MI. While the precision of visual match is greatly
enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation
of SIFT and color features significantly reduces the impact of false positive
matches.
Extensive experiments on several benchmark datasets demonstrate that c-MI
improves the retrieval accuracy significantly, while consuming only half of the
query time compared to the baseline. Importantly, we show that c-MI is well
complementary to many prior techniques. Assembling these methods, we have
obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench
datasets, respectively, which compare favorably with the state-of-the-arts.Comment: 8 pages, 7 figures, 6 tables. Accepted to CVPR 201
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)
turns non-negative matrix factorization (NMF) into a tractable problem.
Recently, a new class of provably-correct NMF algorithms have emerged under
this assumption. In this paper, we reformulate the separable NMF problem as
that of finding the extreme rays of the conical hull of a finite set of
vectors. From this geometric perspective, we derive new separable NMF
algorithms that are highly scalable and empirically noise robust, and have
several other favorable properties in relation to existing methods. A parallel
implementation of our algorithm demonstrates high scalability on shared- and
distributed-memory machines.Comment: 15 pages, 6 figure
Image retrieval with hierarchical matching pursuit
A novel representation of images for image retrieval is introduced in this
paper, by using a new type of feature with remarkable discriminative power.
Despite the multi-scale nature of objects, most existing models perform feature
extraction on a fixed scale, which will inevitably degrade the performance of
the whole system. Motivated by this, we introduce a hierarchical sparse coding
architecture for image retrieval to explore multi-scale cues. Sparse codes
extracted on lower layers are transmitted to higher layers recursively. With
this mechanism, cues from different scales are fused. Experiments on the
Holidays dataset show that the proposed method achieves an excellent retrieval
performance with a small code length.Comment: 5 pages, 6 figures, conferenc
Efficient large-scale image search with a vocabulary tree
The task of searching and recognizing objects in images has become an important research topic in the area of image processing and computer vision. Looking for similar images in large datasets given an input query and responding as fast as possible is a very challenging task. In this work the Bag of Features approach is studied, and an implementation of the visual vocabulary tree method from Nist´er and Stew´enius is presented. Images are described using local invariant descriptor techniques and then indexed in a database using an inverted index for further queries. The descriptors are quantized according to a visual vocabulary, creating sparse vectors, which allows to compute very efficiently, for each query, a ranking of similarity for indexed images. The performance of the method is analyzed varying different factors, such as the parameters for the vocabulary tree construction, different techniques of local descriptors extraction and dimensionality reduction with PCA. It can be observed that the retrieval performance increases with a richer vocabulary and decays very slowly as the size of the dataset grows.Fil: Uriza, Esteban. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Gómez Fernández, Francisco Roberto. Consejo Nacional de Investigaciones CientÃficas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Rais, MartÃn. Escuela Normal Superior de Cachan; Franci
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