Modeling Colors of Single Attribute Variations with Application to Food Appearance
This paper considers the intra-image color-space of an object or a scene when these are subject to a dominant single-source of variation. The source of variation can be intrinsic or extrinsic (i.e., imaging conditions) to the object. We observe that the quantized colors for such objects typically lie on a planar subspace of RGB, and in some cases linear or polynomial curves on this plane are effective in capturing these color variations. We also observe that the inter-image color sub-spaces are robust as long as drastic illumination change is not involved. We illustrate the use of this analysis for: discriminating between shading-change and reflectance-change for patches, and object detection, segmentation and recognition based on a single exemplar. We focus on images of food items to illustrate the effectiveness of the proposed approach.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeObjectobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Texture for Colors: Natural Representations of Colors Using Variable Bit-Depth Textures
Numerous methods have been proposed to transform color and grayscale images to their single bit-per-pixel binary counterparts. Commonly, the goal is to enhance specific attributes of the original image to make it more am…
BinarizationMIMT: Multi-Illuminant Color Constancy via Multi-Task Local Surface and Light Color Learning
The assumption of a uniform light color distribution is no longer applicable in scenes that have multiple light colors. Most color constancy methods are designed to deal with a single light color, and thus are erroneous …
Color ConstancyEdge DetectionMulti-Task LearningBeyond Identity: What Information Is Stored in Biometric Face Templates?
Deeply-learned face representations enable the success of current face recognition systems. Despite the ability of these representations to encode the identity of an individual, recent works have shown that more informat…
AttributeFace RecognitionPrivacy PreservingScalable Visual Attribute Extraction through Hidden Layers of a Residual ConvNet
Visual attributes play an essential role in real applications based on image retrieval. For instance, the extraction of attributes from images allows an eCommerce search engine to produce retrieval results with higher pr…
AttributeAttribute ExtractionImage RetrievalRetrievalFaceController: Controllable Attribute Editing for Face in the Wild
Face attribute editing aims to generate faces with one or multiple desired face attributes manipulated while other details are preserved. Unlike prior works such as GAN inversion, which has an expensive reverse mapping p…
AttributeDisentanglementFace Swapping