39,596 research outputs found
Optimal Geometric Matching for Patch-Based Object Detection
We present an efficient method to determine the optimal matching of two patch-based image object representations under rotation, scaling, and translation (RST). This use of patches is equivalent to a fullyconnected part-based model, for which the presented approach offers an efficient procedure to determine the best fit. While other approaches that use fully connected models have a high complexity in the number of parts used, we achieve linear complexity in that variable, because we only allow RST-matchings. The presented approach is used for object recognition in images: by matching images that contain certain objects to a test image, we can detect whether the test image contains an object of that class or not. We evaluate this approach on the Caltech data and obtain very competitive results
Particular object retrieval with integral max-pooling of CNN activations
Recently, image representation built upon Convolutional Neural Network (CNN)
has been shown to provide effective descriptors for image search, outperforming
pre-CNN features as short-vector representations. Yet such models are not
compatible with geometry-aware re-ranking methods and still outperformed, on
some particular object retrieval benchmarks, by traditional image search
systems relying on precise descriptor matching, geometric re-ranking, or query
expansion. This work revisits both retrieval stages, namely initial search and
re-ranking, by employing the same primitive information derived from the CNN.
We build compact feature vectors that encode several image regions without the
need to feed multiple inputs to the network. Furthermore, we extend integral
images to handle max-pooling on convolutional layer activations, allowing us to
efficiently localize matching objects. The resulting bounding box is finally
used for image re-ranking. As a result, this paper significantly improves
existing CNN-based recognition pipeline: We report for the first time results
competing with traditional methods on the challenging Oxford5k and Paris6k
datasets
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
The primate visual system achieves remarkable visual object recognition
performance even in brief presentations and under changes to object exemplar,
geometric transformations, and background variation (a.k.a. core visual object
recognition). This remarkable performance is mediated by the representation
formed in inferior temporal (IT) cortex. In parallel, recent advances in
machine learning have led to ever higher performing models of object
recognition using artificial deep neural networks (DNNs). It remains unclear,
however, whether the representational performance of DNNs rivals that of the
brain. To accurately produce such a comparison, a major difficulty has been a
unifying metric that accounts for experimental limitations such as the amount
of noise, the number of neural recording sites, and the number trials, and
computational limitations such as the complexity of the decoding classifier and
the number of classifier training examples. In this work we perform a direct
comparison that corrects for these experimental limitations and computational
considerations. As part of our methodology, we propose an extension of "kernel
analysis" that measures the generalization accuracy as a function of
representational complexity. Our evaluations show that, unlike previous
bio-inspired models, the latest DNNs rival the representational performance of
IT cortex on this visual object recognition task. Furthermore, we show that
models that perform well on measures of representational performance also
perform well on measures of representational similarity to IT and on measures
of predicting individual IT multi-unit responses. Whether these DNNs rely on
computational mechanisms similar to the primate visual system is yet to be
determined, but, unlike all previous bio-inspired models, that possibility
cannot be ruled out merely on representational performance grounds.Comment: 35 pages, 12 figures, extends and expands upon arXiv:1301.353
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