Learnable Descriptors for Visual Search

Learnable And Non-learnable Visual Concepts

Valiant’s theory of the learnable is applied to visual concepts in digital pictures. Several visual concepts that are easily perceived by humans are shown to be learnable from positive examples. These concepts include a certain type of inaccurate copies of line drawings, identifying a subset of objects at specific locations, and pictures of lines in a fixed slope. Several characterizations of v...

Learnable Visual Markers

We propose a new approach to designing visual markers (analogous to QR-codes, markers for augmented reality, and robotic fiducial tags) based on the advances in deep generative networks. In our approach, the markers are obtained as color images synthesized by a deep network from input bit strings, whereas another deep network is trained to recover the bit strings back from the photos of these m...

Robust aggregation of local image descriptors for visual search

Visual Image Search: Feature Signatures or/and Global Descriptors

The success of content-based retrieval systems stands or falls with the quality of the utilized similarity model. In the case of having no additional keywords or annotations provided with the multimedia data, the hard task is to guarantee the highest possible retrieval precision using only content-based retrieval techniques. In this paper we push the visual image search a step further by testin...

Kernel Descriptors for Visual Recognition

The design of low-level image features is critical for computer vision algorithms. Orientation histograms, such as those in SIFT [16] and HOG [3], are the most successful and popular features for visual object and scene recognition. We highlight the kernel view of orientation histograms, and show that they are equivalent to a certain type of match kernels over image patches. This novel view all...