Freehand Sketch Recognition Using Deep Features
Freehand sketches often contain sparse visual detail. In spite of the sparsity, they are easily and consistently recognized by humans across cultures, languages and age groups. Therefore, analyzing such sparse sketches can aid our understanding of the neuro-cognitive processes involved in visual representation and recognition. In the recent past, Convolutional Neural Networks (CNNs) have emerged as a powerful framework for feature representation and recognition for a variety of image domains. However, the domain of sketch images has not been explored. This paper introduces a freehand sketch recognition framework based on "deep" features extracted from CNNs. We use two popular CNNs for our experiments -- Imagenet CNN and a modified version of LeNet CNN. We evaluate our recognition framework on a publicly available benchmark database containing thousands of freehand sketches depicting everyday objects. Our results are an improvement over the existing state-of-the-art accuracies by 3% - 11%. The effectiveness and relative compactness of our deep features also make them an ideal candidate for related problems such as sketch-based image retrieval. In addition, we provide a preliminary glimpse of how such features can help identify crucial attributes (e.g. object-parts) of the sketched objects.
Code (0)
등록된 구현이 없습니다.
Tasks
Image RetrievalRetrievalSketch-Based Image RetrievalSketch RecognitionSimilar Papers 제목 키워드 기반
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
Freehand sketching is an inherently sequential process. Yet, most approaches for hand-drawn sketch recognition either ignore this sequential aspect or exploit it in an ad-hoc manner. In our work, we propose a recurrent n…
ObjectObject RecognitionSketch RecognitionFreehand Sketch Generation from Mechanical Components
Drawing freehand sketches of mechanical components on multimedia devices for AI-based engineering modeling has become a new trend. However, its development is being impeded because existing works cannot produce suitable …
SketchyCOCO: Image Generation from Freehand Scene Sketches
We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribu…
AttributeGenerative Adversarial NetworkImage GenerationObject+1SketchBodyNet: A Sketch-Driven Multi-faceted Decoder Network for 3D Human Reconstruction
Reconstructing 3D human shapes from 2D images has received increasing attention recently due to its fundamental support for many high-level 3D applications. Compared with natural images, freehand sketches are much more f…
3D Human Reconstruction3D ReconstructionDecoderFS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context
We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey scene content well but can be sketched within a few mi…
DecoderImage CaptioningImage RetrievalMeta-Learning+1